Group items tagged

until now, the planet’s natural defense systems were better at fighting them off.

Today, climate warming is demolishing those defense systems, driving a catastrophic loss in biodiversity that, when coupled with reckless deforestation and aggressive conversion of wildland for economic development, pushes farms and people closer to the wild and opens the gates for the spread of disease.

ignoring how climate and rapid land development were putting disease-carrying animals in a squeeze was akin to playing Russian roulette.

the virus is believed to have originated with the horseshoe bat, part of a genus that’s been roaming the forests of the planet for 40 million years and thrives in the remote jungles of south China, even that remains uncertain.

China for years and warning that swift climate and environmental change there — in both loss of biodiversity and encroachment by civilization — was going to help new viruses jump to people.

. Roughly 60% of new pathogens come from animals — including those pressured by diversity loss — and roughly one-third of those can be directly attributed to changes in human land use, meaning deforestation, the introduction of farming, development or resource extraction in otherwise natural settings

Vector-borne diseases — those carried by insects like mosquitoes and ticks and transferred in the blood of infected people — are also on the rise as warming weather and erratic precipitation vastly expand the geographic regions vulnerable to contagion.

Climate is even bringing old viruses back from the dead, thawing zombie contagions like the anthrax released from a frozen reindeer in 2016, which can come down from the arctic and haunt us from the past.

It is demonstrating in real time the enormous and undeniable power that nature has over civilization and even over its politics.

it also makes clear that climate policy today is indivisible from efforts to prevent new infectious outbreaks, or, as Bernstein put it, the notion that climate and health and environmental policy might not be related is “a ​dangerous delusion.”

The warming of the climate is one of the principal drivers of the greatest — and fastest — loss of species diversity in the history of the planet, as shifting climate patterns force species to change habitats, push them into new regions or threaten their food and water supplies

What’s known as biodiversity is critical because the natural variety of plants and animals lends each species greater resiliency against threat and together offers a delicately balanced safety net for natural systems

As diversity wanes, the balance is upset, and remaining species are both more vulnerable to human influences and, according to a landmark 2010 study in the journal Nature, more likely to pass along powerful pathogens.

even incremental and seemingly manageable injuries to local environments — say, the construction of a livestock farm adjacent to stressed natural forest — can add up to outsized consequences.

Coronaviruses like COVID-19 aren’t likely to be carried by insects — they don’t leave enough infected virus cells in the blood. But one in five other viruses transmitted from animals to people are vector-borne

the number of species on the planet has already dropped by 20% and that more than a million animal and plant species now face extinction.

Americans have been experiencing this phenomenon directly in recent years as migratory birds have become less diverse and the threat posed by West Nile encephalitis has spread. It turns out that the birds that host the disease happen to also be the tough ones that prevail amid a thinned population

as larger mammals suffer declines at the hands of hunters or loggers or shifting climate patterns, smaller species, including bats, rats and other rodents, are thriving, either because they are more resilient to the degraded environment or they are able to live better among people.

It is these small animals, the ones that manage to find food in garbage cans or build nests in the eaves of buildings, that are proving most adaptable to human interference and also happen to spread disease.

Warmer temperatures and higher rainfall associated with climate change — coupled with the loss of predators — are bound to make the rodent problem worse, with calamitous implications.

As much as weather changes can drive changes in species, so does altering the landscape for new farms and new cities. In fact, researchers attribute a full 30% of emerging contagion to what they call “land use change.”

As the global population surges to 10 billion over the next 35 years, and the capacity to farm food is stressed further again by the warming climate, the demand for land will only get more intense.

Already, more than one-third of the planet’s land surface, and three-quarters of all of its fresh water, go toward the cultivation of crops and raising of livestock. These are the places where infectious diseases spread most often.

The U.S. Centers for Disease Control and Prevention says that fully three-quarters of all new viruses have emerged from animals

Almost every major epidemic we know of over the past couple of decades — SARS, COVID-19, Ebola and Nipah virus — jumped to people from wildlife enduring extreme climate and habitat strain, and still, “we’re naive to them,” she said. “That puts us in a dangerous place.”

A 2008 study in the journal Nature found nearly one-third of emerging infectious diseases over the past 10 years were vector-borne, and that the jumps matched unusual changes in the climate

Ticks and mosquitoes now thrive in places they’d never ventured before. As tropical species move northward, they are bringing dangerous pathogens with them.

by 2050, disease-carrying mosquitoes will ultimately reach 500 million more people than they do today, including some 55 million more Americans.

In 2013, dengue fever — an affliction affecting nearly 400 million people a year, but normally associated with the poorest regions of Africa — was transmitted locally in New York for the first time.

“The long-term risk from dengue may be much higher than COVID,

only 15% of the planet’s forests remain intact. The rest have been cut down, degraded or fragmented to the point that they disrupt the natural ecosystems that depend on them.

it’s only a matter of time before other exotic animal-driven pathogens are driven from the forests of the global tropics to the United States or Canada or Europe because of the warming climate.

it will also shape how easily we get sick. According to a 2013 study in the journal PLOS Currents Influenza, warm winters were predictors of the most severe flu seasons in the following year

Even harsh swings from hot to cold, or sudden storms — exactly the kinds of climate-induced patterns we’re already seeing — make people more likely to get sick.

The chance of a flu epidemic in America’s most populated cities will increase by as much as 50% this century, and flu-related deaths in Europe could also jump by 50%.

Slow action on climate has made dramatic warming and large-scale environmental changes inevitable, he said, “and I think that increases in disease are going to come along with it.”

By late 2018, epidemiologists there were bracing for what they call “spillover,” or the failure to keep a virus locally contained as it jumped from the bats and villages of Yunnan into the wider world.

In late 2018, the Trump administration, as part of a sweeping effort to bring U.S. programs in China to a halt, abruptly shut down the research — and its efforts to intercept the spread of a new novel coronavirus along with it. “We got a cease and desist,” said Dennis Carroll, who founded the PREDICT program and has been instrumental in global work to address the risks from emerging viruses. By late 2019, USAID had cut the program’s global funding.

The loss is immense. The researchers believed they were on the cusp of a breakthrough, racing to sequence the genes of the coronaviruses they’d extracted from the horseshoe bat and to begin work on vaccines.

They’d campaigned for years for policymakers to fully consider what they’d learned about how land development and climate changes were driving the spread of disease, and they thought their research could literally provide governments a map to the hot spots most likely to spawn the next pandemic.

They also hoped the genetic material they’d collected could lead to a vaccine not just for one lethal variation of COVID, but perhaps — like a missile defense shield for the biosphere — to address a whole family of viruses at once

Carroll said knowledge of the virus genomes had the potential “to totally transform how we think about future biomedical interventions before there’s an emergence.

PREDICT’s staff and advisers have pushed the U.S. government to consider how welding public health policy with environmental and climate science could help stem the spread of contagions.

Since Donald Trump was elected, the group hasn’t been invited back.

What Daszak really wants — in addition to restored funding to continue his work — is the public and leaders to understand that it’s human behavior driving the rise in disease, just as it drives the climate crisis

“We turn a blind eye to the fact that our behavior is driving this,” he said. “We get cheap goods through Walmart, and then we pay for it forever through the rise in pandemics. It’s upside down.”

Such a regime, in his view, would keep the trappings of democracy, including seemingly free elections, while leaders would control the election process, the media and the scope of permissible debate. “What you get is a country that is de facto less free.”

The use of digital technology in the 2016 election “represents the latest chapter in the disintegration of legacy institutions that had set bounds for American politics in the postwar era,

the Trump campaign was “totally unprecedented in its breaking of established norms of politics.” He argues thatthis type of campaign is only successful in a context in which certain established institutions — particularly, the mainstream media and political party organizations — have lost most of their power, both in the United States and around the world.

The influence of the internet is only the most recent manifestation of the weakening of the two major American political parties over the past century, with the Civil Service undermining patronage, the rise of mass media altering communication, campaign finance law empowering donors independent of the parties, and the ascendance of direct primaries gutting the power of party bosses to pick nominees.

how the erosion of political parties played out in 2016:Neither party appeared to have a mechanism of internal correction. Neither could muster the wise elders to steer a more conventional course. Neither could use its congressional leadership to regain control of the party through its powers of governance. Neither could lay claim to financial resources that would compel a measure of candidate loyalty. Neither could even exert influence though party endorsements.

The parties proved hollow vehicles that offered little organizational resistance to capture by outsiders. And what was captured appeared little more than a brand, certainly not the vibrant organizations that are heralded as the indispensable glue of democratic politics.

“We are witnessing a period of deep challenge to the core claims of democracy to be the superior form of political organization of civilized peoples,” he told his audience:

Put another way, media is increasingly less just a source of information, and it is increasingly more a site of coordination, because groups that see or hear or watch or listen to something can now gather around and talk to each other as well.

“Technology has overtaken one of the basic functions you needed political parties for in the past, communication with voters,” he said. “Social media has changed all of that, candidates now have direct access through email, blogs and Twitter,” along with Facebook, Instagram, Snapchat and other platforms.

Two developments in the 2016 campaign provided strong evidence of the vulnerability of democracies in the age of the internet: the alleged effort of the Russian government to secretly intervene on behalf of Trump, and the discovery by internet profiteers of how to monetize the distribution of fake news stories, especially stories damaging to Hillary Clinton.

Clay Shirky is a professor in the Interactive Telecommunications Program at N.Y.U. In a 2009 TED talk — the full political significance of which has only become clear over the past eight years — he described some of the implications of the digital revolution:

The internet is the first medium in history that has native support for groups and conversation at the same time. Whereas the phone gave us the one-to-one pattern, and television, radio, magazines, books, gave us the one-to-many pattern, the internet gives us the many-to-many pattern.

The second big change is that, as all media gets digitized, the internet also becomes the mode of carriage for all other media, meaning that phone calls migrate to the internet, magazines migrate to the internet, movies migrate to the internet

The current moment of democratic uncertainty draws from four central institutional challenges, each one a compromise of how democracy was consolidated over the past few centuries. First, the accelerated decline of political parties and other institutional forms of engagement; second, the weakness of the legislative branches; third, the loss of a sense of social cohesion; and fourth, the decline in democratic state competence.

the third big change, according to Shirky, is that members of the former audiencecan now also be producers and not consumers. Every time a new consumer joins this media landscape a new producer joins as well, because the same equipment — phones, computers — let you consume and produce

Of course, this problem goes much deeper than the internet

Our politics are vulnerable to nefarious influences — whether of the Kremlin variety or the Breitbart variety — not because our information landscape is open and fluid, but because voters’ perceptions have become untethered from reality. For reasons that are both complex and debatable, very many voters have stopped seeing government as a tool for the production of the common good, and have instead turned to politicians (and others) who at least make them feel good. Thus, the news we consume has become as much about emotion and identity as about facts. That’s where the vulnerability comes in, and its roots are in our politics — not in the internet.

In justifying this violence, supporters of slavery recast it as a kind of self-defense, and violence committed in self-defense—unlike violence committed out of anger or hatred—says nothing about the character of the perpetrators.

The demise of slavery did not lead to a decline in white-on-black violence—it merely changed forms.

Most notoriously, whites lynched blacks; looking just at white-on-black lynchings for the purpose of racial control, the Alabama-based Equal Justice Initiative documented 4,075 lynchings between 1877, when Reconstruction ended, and 1950

Lynchings were even more savage and sadistic than most people imagine.

Patrick Phillips recounts one such expulsion in his 2016 book Blood at the Root. In 1912, the white residents of Forsyth County, Georgia, drove out all black residents of the county, who abandoned their land and their belongings as they fled bands of “night riders”

again, because defenders of lynching portrayed their violence as justified, even as we recognize today that lynching is a thing white people did, we can believe that it implies nothing about what it meant to be white.

The motifs of black men’s savage, uncontrollable lust and of white women’s chastity and virtue combined in a perfect storm of white fear to justify the practice of lynching generally, even when a particular lynching was not alleged to be in response to rape.

Even opponents of lynching seemed to agree that the supposed epidemic of black-on-white rape demanded a violent solution.

Attempts to justify racial terror have  been accompanied by a national commitment to erasing it from our memory.

Consider the little-known history of racial cleansing in America. Across the South and Midwest, Pulitzer Prize-winning journalist Elliot Jaspin has identified hundreds of counties that experienced sharp and abrupt drop-offs of their black populations. Though the historical record, as with lynching, is intentionally spotty, in many cases there is clear evidence that whites systematically and violently drove out entire black populations, dispossessing black families of their property and rendering them refugees.

Such depravity required justification. This time, accusations of black criminality would do the trick. In most lynchings, the victim was accused of rape, murder, or both.

This state of affairs is not inevitable; America’s relationship with its history is a collective choice. Other countries have made other choices in the wake of systematic racist violence.

since black refugees could not safely return to retrieve their personal property or to sell their land, the new white “owners” could assert adverse possession after the statutory period of seven years, registering deeds at the county courthouse even though the Georgia statute technically required adverse possession to be “peaceable.”

This literal erasure enabled the ensuing rhetorical erasure of Forsyth’s history. In 1987, civil rights activists staged a protest in Forsyth County, which remained all-white 75 years after its racial cleansing. The white residents of the county seemed genuinely puzzled as to why activists were harassing them.

White terrorists cleansed their communities of blacks in Forsyth County in 1912, and in Marshall County, Kentucky in 1908, and in Vermillion County, Indiana in 1923, and in Sharp County, Arkansas on Christmas Eve, 1906, to name just a few. If we recognized these crimes for what they were, it would be difficult to ignore that a pattern of racial terrorism had occurred across decades, and that later incidents of racial violence were part of that pattern.

But a crime no one remembers cannot lead us to associate its perpetrators with criminality, so our concept of whiteness remains untouched by any association with violence.   

We have erased the history of lynching in much the same way. The overwhelming majority of lynching sites remain unmarked and un-memorialized. The failure to mark these sites allows Americans (especially whites) to forget the atrocities.

This erasure of history from public spaces contributes to a kind of gaslighting dynamic in discussions of race in America: whites are able to minimize blacks’ claims of historical and ongoing oppression because they have successfully forgotten much of the history that blacks still remember.  

Justifying and erasing hundreds of years of white-on-black violence has left many Americans ill-equipped to make sense of the racist violence that we live with today. As a result, whites often lack the vocabulary to contextualize even the most obviously racist events.

media outlets tended to use vague language like “evil” or “monster” to describe Roof. That kind of equivocal terminology implied that Roof had an inherent, inevitable propensity for violence, a propensity that sprang up organically rather than being nurtured by extremist influences

But Roof’s motive could not have been clearer. He wrote a manifesto about the threat black criminality poses to Western civilization. He confessed to police that he planned and carried out the shooting with the intention of starting a “race war.” Roof was desperate to make sure everyone understood that he had acted to promote white supremacy, but many Americans stubbornly refused to take him at his own word, insisting that it would be premature to interpret the shooting as racial terrorism.

By failing to connect Roof’s racism with his obvious criminality, many allowed themselves to think of the Charleston tragedy as an isolated and senseless act, instead of seeing it as a seamless continuation of the racial terrorism that is America’s heritage.

In every generation, Americans have consistently spoken about racist violence committed by white people as justified, erased it from memory, or simply called it something else

So when self-identified white nationalists seeking to turn America into a whites-only homeland held a torchlight protest, many observers failed to make the clear connection to bands of night riders, armed and carrying torches, driving blacks out of their homes to cleanse the region of anyone not white.

less than a decade later, Phillips writes, the white leaders of Forsyth County were boasting—with a straight face—that “while other north Georgia communities [with mixed populations] continued to suffer episodes of ‘race trouble,’ there were no such embarrassments in Forsyth.” This was possible in part because they had erased evidence of just how Forsyth came to be an all-white county

the effort is underway to face up to the full horror of racist terror, not justify it; to name racially motivated violence as such, not hide it; to mark down in disgrace the names of the perpetrators, not celebrate them; and to memorialize the victims, not erase their suffering.

America, tragically, is over a century behind on its obligation to undertake the same reckoning.

America, tragically, is over a century behind on its obligation to undertake the same reckoning. The horrific events of August provide an opportunity to get started.

From supply failures of essential equipment to the summer exams debacle to the care homes scandal, another pattern of the crisis has been attempts to swerve culpability for all the things that have gone wrong by blaming anyone else but ministers.

One former Tory cabinet minister remarks: “There’s bound to be a public inquiry. We will be held to account for the fact that our deaths are higher and our recession is deeper.”

The pattern is one of resisting taking the necessary steps at the time when they would have been most effective and then being compelled to implement them late and with more damaging effect

Given his lack of dedication to detail and the hard grind of delivering competent government, he needed a capable cabinet. Feebly fearful of having any substantial figures around the top table who might challenge him, he instead surrounded himself with a cabinet characterised by Tory MPs as “lightweight”, “talentless”, “loyalist duds” and “nodding dogs”.

Another persistent pattern during this plague year has been to over-promise and under-deliver. We were going to have a “fantastic” this and a “world-beating” that and a “moon-shot” the other. We would have settled for a test, trace and isolate programme that worked.

Optimism can be a positive trait in a politician, but wishful thinking is a fatal characteristic in an epidemic. So is deceptive messaging to the public.

That misjudgment, like all the other ones, flows from his personality. Just below the surface of his performative face lurks an insecure character who trusts no one and yearns to be loved by everyone. He hates being the bearer of bad news and tough choices.

Again and again, the Facebook Papers show staffers sounding alarms about the dangers posed by the platform—how Facebook amplifies extremism and misinformation, how it incites violence, how it encourages radicalization and political polarization. Again and again, staffers reckon with the ways in which Facebook’s decisions stoke these harms, and they plead with leadership to do more.

And again and again, staffers say, Facebook’s leaders ignore them.

Facebook has dismissed the concerns of its employees in manifold ways.

One of its cleverer tactics is to argue that staffers who have raised the alarm about the damage done by their employer are simply enjoying Facebook’s “very open culture,” in which people are encouraged to share their opinions, a spokesperson told me. This stance allows Facebook to claim transparency while ignoring the substance of the complaints, and the implication of the complaints: that many of Facebook’s employees believe their company operates without a moral compass.

When you stitch together the stories that spanned the period between Joe Biden’s election and his inauguration, it’s easy to see Facebook as instrumental to the attack on January 6. (A spokesperson told me that the notion that Facebook played an instrumental role in the insurrection is “absurd.”)

what emerges from a close reading of Facebook documents, and observation of the manner in which the company connects large groups of people quickly, is that Facebook isn’t a passive tool but a catalyst. Had the organizers tried to plan the rally using other technologies of earlier eras, such as telephones, they would have had to identify and reach out individually to each prospective participant, then persuade them to travel to Washington. Facebook made people’s efforts at coordination highly visible on a global scale.

The platform not only helped them recruit participants but offered people a sense of strength in numbers. Facebook proved to be the perfect hype machine for the coup-inclined.

In November 2019, Facebook staffers noticed they had a serious problem. Facebook offers a collection of one-tap emoji reactions. Today, they include “like,” “love,” “care,” “haha,” “wow,” “sad,” and “angry.” Company researchers had found that the posts dominated by “angry” reactions were substantially more likely to go against community standards, including prohibitions on various types of misinformation, according to internal documents.

In July 2020, researchers presented the findings of a series of experiments. At the time, Facebook was already weighting the reactions other than “like” more heavily in its algorithm—meaning posts that got an “angry” reaction were more likely to show up in users’ News Feeds than posts that simply got a “like.” Anger-inducing content didn’t spread just because people were more likely to share things that made them angry; the algorithm gave anger-inducing content an edge. Facebook’s Integrity workers—employees tasked with tackling problems such as misinformation and espionage on the platform—concluded that they had good reason to believe targeting posts that induced anger would help stop the spread of harmful content.

By dialing anger’s weight back to zero in the algorithm, the researchers found, they could keep posts to which people reacted angrily from being viewed by as many users. That, in turn, translated to a significant (up to 5 percent) reduction in the hate speech, civic misinformation, bullying, and violent posts—all of which are correlated with offline violence—to which users were exposed.

Facebook rolled out the change in early September 2020, documents show; a Facebook spokesperson confirmed that the change has remained in effect. It was a real victory for employees of the Integrity team.

But it doesn’t normally work out that way. In April 2020, according to Frances Haugen’s filings with the SEC, Facebook employees had recommended tweaking the algorithm so that the News Feed would deprioritize the surfacing of content for people based on their Facebook friends’ behavior. The idea was that a person’s News Feed should be shaped more by people and groups that a person had chosen to follow. Up until that point, if your Facebook friend saw a conspiracy theory and reacted to it, Facebook’s algorithm might show it to you, too. The algorithm treated any engagement in your network as a signal that something was worth sharing. But now Facebook workers wanted to build circuit breakers to slow this form of sharing.

Experiments showed that this change would impede the distribution of hateful, polarizing, and violence-inciting content in people’s News Feeds. But Zuckerberg “rejected this intervention that could have reduced the risk of violence in the 2020 election,” Haugen’s SEC filing says. An internal message characterizing Zuckerberg’s reasoning says he wanted to avoid new features that would get in the way of “meaningful social interactions.” But according to Facebook’s definition, its employees say, engagement is considered “meaningful” even when it entails bullying, hate speech, and reshares of harmful content.

This episode, like Facebook’s response to the incitement that proliferated between the election and January 6, reflects a fundamental problem with the platform

Facebook’s megascale allows the company to influence the speech and thought patterns of billions of people. What the world is seeing now, through the window provided by reams of internal documents, is that Facebook catalogs and studies the harm it inflicts on people. And then it keeps harming people anyway.

“I am worried that Mark’s continuing pattern of answering a different question than the question that was asked is a symptom of some larger problem,” wrote one Facebook employee in an internal post in June 2020, referring to Zuckerberg. “I sincerely hope that I am wrong, and I’m still hopeful for progress. But I also fully understand my colleagues who have given up on this company, and I can’t blame them for leaving. Facebook is not neutral, and working here isn’t either.”

It is quite a thing to see, the sheer number of Facebook employees—people who presumably understand their company as well as or better than outside observers—who believe their employer to be morally bankrupt.

I spoke with several former Facebook employees who described the company’s metrics-driven culture as extreme, even by Silicon Valley standards

Facebook workers are under tremendous pressure to quantitatively demonstrate their individual contributions to the company’s growth goals, they told me. New products and features aren’t approved unless the staffers pitching them demonstrate how they will drive engagement.

e worries have been exacerbated lately by fears about a decline in new posts on Facebook, two former employees who left the company in recent years told me. People are posting new material less frequently to Facebook, and its users are on average older than those of other social platforms.

One of Facebook’s Integrity staffers wrote at length about this dynamic in a goodbye note to colleagues in August 2020, describing how risks to Facebook users “fester” because of the “asymmetrical” burden placed on employees to “demonstrate legitimacy and user value” before launching any harm-mitigation tactics—a burden not shared by those developing new features or algorithm changes with growth and engagement in mind

The note said:We were willing to act only after things had spiraled into a dire state … Personally, during the time that we hesitated, I’ve seen folks from my hometown go further and further down the rabbithole of QAnon and Covid anti-mask/anti-vax conspiracy on FB. It has been painful to observe.

Current and former Facebook employees describe the same fundamentally broken culture—one in which effective tactics for making Facebook safer are rolled back by leadership or never approved in the first place.

That broken culture has produced a broken platform: an algorithmic ecosystem in which users are pushed toward ever more extreme content, and where Facebook knowingly exposes its users to conspiracy theories, disinformation, and incitement to violence.

One example is a program that amounts to a whitelist for VIPs on Facebook, allowing some of the users most likely to spread misinformation to break Facebook’s rules without facing consequences. Under the program, internal documents show, millions of high-profile users—including politicians—are left alone by Facebook even when they incite violence

whitelisting influential users with massive followings on Facebook isn’t just a secret and uneven application of Facebook’s rules; it amounts to “protecting content that is especially likely to deceive, and hence to harm, people on our platforms.”

Facebook workers tried and failed to end the program. Only when its existence was reported in September by The Wall Street Journal did Facebook’s Oversight Board ask leadership for more information about the practice. Last week, the board publicly rebuked Facebook for not being “fully forthcoming” about the program.

As a result, Facebook has stoked an algorithm arms race within its ranks, pitting core product-and-engineering teams, such as the News Feed team, against their colleagues on Integrity teams, who are tasked with mitigating harm on the platform. These teams establish goals that are often in direct conflict with each other.

“We can’t pretend we don’t see information consumption patterns, and how deeply problematic they are for the longevity of democratic discourse,” a user-experience researcher wrote in an internal comment thread in 2019, in response to a now-infamous memo from Andrew “Boz” Bosworth, a longtime Facebook executive. “There is no neutral position at this stage, it would be powerfully immoral to commit to amorality.”

Zuckerberg has defined Facebook’s mission as making “social infrastructure to give people the power to build a global community that works for all of us,” but in internal research documents his employees point out that communities aren’t always good for society:

When part of a community, individuals typically act in a prosocial manner. They conform, they forge alliances, they cooperate, they organize, they display loyalty, they expect obedience, they share information, they influence others, and so on. Being in a group changes their behavior, their abilities, and, importantly, their capability to harm themselves or others

Thus, when people come together and form communities around harmful topics or identities, the potential for harm can be greater.

The infrastructure choices that Facebook is making to keep its platform relevant are driving down the quality of the site, and exposing its users to more dangers

hose dangers are also unevenly distributed, because of the manner in which certain subpopulations are algorithmically ushered toward like-minded groups

And the subpopulations of Facebook users who are most exposed to dangerous content are also most likely to be in groups where it won’t get reported.

And it knows that 3 percent of Facebook users in the United States are super-consumers of conspiracy theories, accounting for 37 percent of known consumption of misinformation on the platform.

Zuckerberg’s positioning of Facebook’s role in the insurrection is odd. He lumps his company in with traditional media organizations—something he’s ordinarily loath to do, lest the platform be expected to take more responsibility for the quality of the content that appears on it—and suggests that Facebook did more, and did better, than journalism outlets in its response to January 6. What he fails to say is that journalism outlets would never be in the position to help investigators this way, because insurrectionists don’t typically use newspapers and magazines to recruit people for coups.

Facebook wants people to believe that the public must choose between Facebook as it is, on the one hand, and free speech, on the other. This is a false choice. Facebook has a sophisticated understanding of measures it could take to make its platform safer without resorting to broad or ideologically driven censorship tactics.

Facebook knows that no two people see the same version of the platform, and that certain subpopulations experience far more dangerous versions than others do

Facebook knows that people who are isolated—recently widowed or divorced, say, or geographically distant from loved ones—are disproportionately at risk of being exposed to harmful content on the platform.

It knows that repeat offenders are disproportionately responsible for spreading misinformation.

All of this makes the platform rely more heavily on ways it can manipulate what its users see in order to reach its goals. This explains why Facebook is so dependent on the infrastructure of groups, as well as making reshares highly visible, to keep people hooked.

It could consistently enforce its policies regardless of a user’s political power.

Facebook could ban reshares.

It could choose to optimize its platform for safety and quality rather than for growth.

It could tweak its algorithm to prevent widespread distribution of harmful content.

Facebook could create a transparent dashboard so that all of its users can see what’s going viral in real time.

It could make public its rules for how frequently groups can post and how quickly they can grow.

It could also automatically throttle groups when they’re growing too fast, and cap the rate of virality for content that’s spreading too quickly.

Facebook could shift the burden of proof toward people and communities to demonstrate that they’re good actors—and treat reach as a privilege, not a right

You must be vigilant about the informational streams you swim in, deliberate about how you spend your precious attention, unforgiving of those who weaponize your emotions and cognition for their own profit, and deeply untrusting of any scenario in which you’re surrounded by a mob of people who agree with everything you’re saying.

It could do all of these things. But it doesn’t.

Lately, people have been debating just how nefarious Facebook really is. One argument goes something like this: Facebook’s algorithms aren’t magic, its ad targeting isn’t even that good, and most people aren’t that stupid.

All of this may be true, but that shouldn’t be reassuring. An algorithm may just be a big dumb means to an end, a clunky way of maneuvering a massive, dynamic network toward a desired outcome. But Facebook’s enormous size gives it tremendous, unstable power.

Facebook takes whole populations of people, pushes them toward radicalism, and then steers the radicalized toward one another.

When the most powerful company in the world possesses an instrument for manipulating billions of people—an instrument that only it can control, and that its own employees say is badly broken and dangerous—we should take notice.

The lesson for individuals is this:

Facebook could say that its platform is not for everyone. It could sound an alarm for those who wander into the most dangerous corners of Facebook, and those who encounter disproportionately high levels of harmful content

Without seeing how Facebook works at a finer resolution, in real time, we won’t be able to understand how to make the social web compatible with democracy.

Before the announcement of the recent mass conscription, which was followed by the declaration of martial law in Russian-occupied areas of Ukraine, about 30 percent of Russians who were asked by a pro-Kremlin pollster to describe the predominant mood of people around them said they were anxious. Since the decree, that number has risen to 69 percent.

as the invasion has dragged on, Mr. Putin has had to shout “Wolf!” ever more stridently.

With little experience of democracy, Russians have traditionally been content to entrust their government to a strong “khoziayin,” or master, so long as he provides stability and seems to know what he’s doing. But if that contract is violated, as Russia’s greatest poet, Alexander Pushkin, warned in a quote every Russia knows by heart, “God save us from seeing a Russian revolt, senseless and merciless.”

He has drawn liberally on his rewriting of Russian history and culture — as he did again in the Valdai speech, citing, among others, Solzhenitsyn and Dostoyevsky — to claim a spiritual superiority over a West he sees as decadent and decaying. Never mind that these two writers were both repressed by the state, Soviet and czarist, and that Russia is widely perceived as a kleptocracy.

There is no soft power in this equation, no appreciation of reasons Ukraine might be more attracted to Europe than to Russia, but only spheres of control parceled out according to rules of conquest and control that the West rejected after World War II. The longings of the Ukrainians have no part in this; Russia’s — Mr. Putin’s — mission is to return to Russia what is Russia’s by right of might

it is becoming evident that Mr. Putin, increasingly isolated during the Covid pandemic, was led to believe by his sycophantic lieutenants that a quick invasion would promptly topple the Kyiv government and herd Ukraine back into the fold, and that the West was too far gone to do anything about it.

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.

With greater accuracy, for example, marketers can comb large databases of consumer behavior to get more precise information on buying habits. And improvements in facial recognition are likely to make surveillance technology cheaper and more commonplace.

Modern artificial neural networks are composed of an array of software components, divided into inputs, hidden layers and outputs. The arrays can be “trained” by repeated exposures to recognize patterns like images or sounds.

What matters isn’t how much money you have, they argue, but how much stuff you have, and the income gap isn’t matched by a comparable consumption gap.

From reading Hassett and Mathur’s op-ed piece, you would never know that there’s a disagreement among serious academics on the basic question of whether consumption patterns match income patterns or not.

Not all consumption is created equal. Which items are cheaper? Which are more expensive? Hassett and Mathur don’t say. The answer is that clothing and food and electronics are cheaper (Hassett and Mathur marvel that even low-income people often own microwave ovens and cell phones) while housing, transportation (read: automobiles), higher education, and health care are more expensive.

Health care is especially expensive when people have no health insurance, or lousy health insurance. In 2010, for instance, Latinos saw their health care expenditures rise by 17 percent (i.e, by $274), while non-Latinos (who tend to be wealthier) saw their health care expenditures rise by a mere one percent (i.e., by $4). On a per-dollar basis, Latinos “consumed” more health care than non-Latinos. But obviously that’s a meaningless statistic. The non-Latinos had cheaper access to health care, which is what matters.

To buy stuff you need money, right? Hassett and Mathur pretend this question doesn’t exist, but the answer is obvious. If the middle class really is keeping up with the affluent Joneses, it’s through borrowing. And in fact, a growing body of work (including this recent Century Foundation paper) suggests that income inequality has been driving America’s debt binge.

In his hedged forecasts this time, Silver appears to be acknowledging that polling and historical patterns don’t always capture what John Maynard Keynes, in his classic 1936 economic General Theory, described as “animal spirits.”

There is, Keynes wrote, “the instability due to the characteristic of human nature that a large proportion of our positive activities depend on spontaneous optimism rather than on a mathematical expectation, whether moral or hedonistic or economic. Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as a result of animal spirits — of a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities.”

Last month’s manifesto not only declared Islam incompatible with German legal and cultural values, but also endorsed a ban on burqas and the call to prayer.

First, despite having a woman at the helm in the figure of Frauke Petry (as well as trigger-happy aristocrat Beatrix von Storch, who has advocated using deadly force  against illegal migrants at the border, as deputy party chief), AfD supporters are predominantly male.

As the German daily Die Zeit pointed out, that means AfD support follows roughly the same pattern as support for the intensely anti-Islamic pan-European movement PEGIDA (“Patriotic Europeans Against the Islamization of the West”).

Theories abound as to why and to what extent men are more likely to vote for far-right or xenophobic platforms than women—a pattern that holds with Trump supporters in the United States, as well as voters for Austria’s far-right presidential candidate Norbert Hofer, who just barely lost that country’s election this week.

A second trend in AfD demographics involves class. Originally, professors, journalists, and business leaders dominated the party, with over half the founding members in 2013 sporting a “Dr.” in front of their names.

Third comes age. “[AfD supporters] are youngish to middle-aged,” said Arzheimer. “Interestingly, voters over 60 seem to shy from voting for the AfD because they're still tied to the Christian Democrats,” Merkel’s center-right party.

When Bernd Lucke founded the AfD, he intended to win voters both from the Christian Democrats and Germany’s liberal party, the Free Democratic Party (FDP).

Crucially, Arzheimer pointed out, the AfD manages to attract NPD voters while also remaining “acceptable for a much larger group of the German population.”

Part of AfD’s strength so far has been its ability to capitalize on intense concerns about the economy and immigration with increasingly inflammatory rhetoric while maintaining a sheen of respectability—crucial in German politics, where incitement to ethnic or racial hatred is a criminal offense.

The AfD’s fragility may be what sets it apart both from right-wing parties further east and the newly nativist turn in the United States.

Art made a similar point, but turned westward. “There’s been a major containment of this far-right nativism in Germany … but it’s the United States in which it’s become in fact a part of the political system.”

There’s a term in German, he mused: ausgegrenzt, translating roughly to “excluded” or “marginalized,” but with a literal translation closer to “beyond limits” or “out of bounds.” Those who wanted the NPD banned wanted it “ausgegrenzt.”

it reflects conservative belief in the magic of the marketplace, in the superiority of free-market competition over government planning.

despite many promises that prison privatization will lead to big cost savings, such savings — as a comprehensive study by the Bureau of Justice Assistance, part of the U.S. Department of Justice, concluded — “have simply not materialized.”

To the extent that private prison operators do manage to save money, they do so through “reductions in staffing patterns, fringe benefits, and other labor-related costs.”

So what’s really behind the drive to privatize prisons, and just about everything else?

One answer is that privatization can serve as a stealth form of government borrowing, in which governments avoid recording upfront expenses (or even raise money by selling existing facilities) while raising their long-run costs in ways taxpayers can’t see. We hear a lot about the hidden debts that states have incurred in the form of pension liabilities; we don’t hear much about the hidden debts now being accumulated in the form of long-term contracts with private companies hired to operate prisons, schools and more.

Another answer is that privatization is a way of getting rid of public employees, who do have a habit of unionizing and tend to lean Democratic in any case.

But the main answer, surely, is to follow the money. Never mind what privatization does or doesn’t do to state budgets; think instead of what it does for both the campaign coffers and the personal finances of politicians and their friends. As more and more government functions get privatized, states become pay-to-play paradises, in which both political contributions and contracts for friends and relatives become a quid pro quo for getting government business

nonprivatized government has its own problems of undue influence, that prison guards and teachers’ unions also have political clout, and this clout sometimes distorts public policy. Fair enough. But such influence tends to be relatively transparent. Everyone knows about those arguably excessive public pensions; it took an investigation by The Times over several months to bring the account of New Jersey’s halfway-house-hell to light.

It is, instead, almost surely a glimpse of a pervasive and growing reality, of a corrupt nexus of privatization and patronage that is undermining government across much of our nation.

in the past 30 years, software and robots have thrived at replacing a particular kind of occupation: the average-wage, middle-skill, routine-heavy worker, especially in manufacturing and office admin. 

the next wave of computer progress will continue to shred human work where it already has: manufacturing, administrative support, retail, and transportation. Most remaining factory jobs are "likely to diminish over the next decades," they write. Cashiers, counter clerks, and telemarketers are similarly endangered

here's a chart of the ten jobs with a 99-percent likelihood of being replaced by machines and software. They are mostly routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers, and insurance underwriters)

I've also listed the dozen jobs they consider least likely to be automated. Health care workers, people entrusted with our safety, and management positions dominate the list.

If you wanted to use this graph as a guide to the future of automation, your upshot would be: Machines are better at rules and routines; people are better at directing and diagnosing. But it doesn't have to stay that way.

Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats

Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they're bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do

lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can't answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.

robots are finally crossing these moats by moving and thinking like people. Amazon has bought robots to work its warehouses. Narrative Science can write earnings summaries that are indistinguishable from wire reports. We can say to our phones I'm lost, help and our phones can tell us how to get home. 

In a decade, the idea of computers driving cars went from impossible to boring.

The first wave showed that machines are better at assembling things. The second showed that machines are better at organization things. Now data analytics and self-driving cars suggest they might be better at pattern-recognition and driving. So what are we better at?

One conclusion to draw from this is that humans are, and will always be, superior at working with, and caring for, other humans. In this light, automation doesn't make the world worse. Far from it: It creates new opportunities for human ingenuity.  

But robots are already creeping into diagnostics and surgeries. Schools are already experimenting with software that replaces teaching hours. The fact that some industries have been safe from automation for the last three decades doesn't guarantee that they'll be safe for the next one.

It would be anxious enough if we knew exactly which jobs are next in line for automation. The truth is scarier. We don't really have a clue.