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Over much opposition from developing countries, the U.S. has insisted that the fund (technically named the Climate Impact and Response Fund) will be housed at the World Bank, where the U.S. holds a majority stake; every World Bank president has been a U.S. citizen. The U.S. also insisted that contributing to the fund not be obligatory. Sue Biniaz, the deputy special envoy for climate at the State Department, said earlier this year that she “violently opposes” arguments that developed countries have a legal obligation under the UN framework to pay into the fund.

The text agreed upon in Dubai on Thursday appears to strike a delicate balance: The fund will indeed be housed at the World Bank, at least for four years, but it will be run according to direction provided at the UN climate gatherings each year, and managed by a board where developed nations are designated fewer than half the seats.

That board’s decisions will supersede those of the World Bank “where appropriate.” Small island nations, which are threatened by extinction because of sea-level rise, will have dedicated seats. Countries that are not members of the World Bank will still be able to access the fund.

the U.S. remains adamant that the fund does not amount to compensation for past emissions, and it rejects any whiff of suggestions that it is liable for other countries’ climate damages.

Even the name “loss and damage,” with its implication of both harm and culpability, has been contentious among delegates

Several countries immediately announced their intended contribution to the fund. The United Arab Emirates and Germany each said they would give $100 million. The U.K. pledged more than$50 million, and Japan committed to $10 million. The U.S. said it would provide $17.5 million, a small number given its responsibility for the largest historical share of global emissions.

Total commitments came in on the order of hundreds of  millions, far shy of an earlier goal of $100 billion a year.

Other donations may continue to trickle in. But the sum is paltry considering researchers recently concluded that 55 climate-vulnerable countries have incurred $525 billion in climate-related losses from 2000 to 2019, depriving them of 20 percent of the wealth they would otherwise have

Still, it’s a big change in how climate catastrophe is treated by developed nations. For the first time, the countries most responsible for climate change are collectively, formally claiming some of that responsibility

One crucial unresolved variable is whether countries such as China and Saudi Arabia—still not treated as “developed” nations under the original UN climate framework—will acknowledge their now-outsize role in worsening climate change by contributing to the fund.

Another big question now will be whether the U.S. can get Congress to agree to payments to the fund, something congressional Republicans are likely to oppose.

Influence by oil and gas industry interests—arguably the entities truly responsible for driving climate change—now delays even public funding of global climate initiatives, he said. “The fossil-fuel industry has successfully convinced the world that loss and damage is something the taxpayer should pay for.” And yet, Whitehouse told me that the industry lobbies against efforts to use public funding this way, swaying Congress and therefore hobbling the U.S.’s ability to uphold even its meager contributions to international climate funding.

But I do think it’s going to be a while before this virus becomes completely normal. And I’ve never been convinced that this current generation of elderly people will ever get to a place where it is completely normal. If you’re 65 or 75 or even older — it’s really hard to teach an immune system new tricks if you’re that age

And so while we may see excess mortality in the elderly decline somewhat, I don’t think we’ll see it ever disappear for this generation who was already old when the pandemic hit. Many will never develop that robust, long-term immunological memory we would want to see — and which happens naturally to someone who’s been exposed hundreds of times since they were a little baby.

There’s a similar story with measles. There is no routine later-life sequelae, like shingles, for measles. But what we do see is that, in measles outbreaks today, there are some people who were vaccinated who get it anyway. Maybe 5 to 15 percent of cases are not immunologically naïve people, but vaccinated people.

Is it really the case that, as babies, we are fighting off those viruses hundreds of times?The short answer is yeah. We start seeing viruses when we’re 2 months old, when we’re a month old. And a lot of these viruses we’ve seen literally tens, if not hundreds of times for some people by the time we’re adults. People tend to think that immunity is binary — you’re either immune or you’re not. That couldn’t be farther from the truth. It’s a gradient, and your protection gets stronger the more times you see a virus.

We used to think we just had this spectacular immune response when we first encountered the virus at, say, age 6, and that the immune response lasted until we were 70. But actually what we were seeing was the effect of an immune system being retrained every time it came into contact with the virus after the initial infection — at 6, and 7, and 8, and so on. Every time your friend got chickenpox, or your neighbor, you got a massive boost. You were re-upping your immune response and diversifying your immunological tools — potentially multiple times a year, a kind of natural booster.

But now, in America, kids get chickenpox vaccines. So you don’t have kids in America getting chickenpox today, and never will. But that means that older Americans, who did get it as kids, are not being exposed again — certainly not multiple times each year. And it turns out that, in the absence of routine re-exposures, that first exposure alone isn’t nearly as good at driving lifelong immunity and warding off shingles until your immune system begins to fall apart in old age — it can last until you’re in your 30s, for example but not until your 70s.

With Covid, when it infects you, it can land in your upper respiratory tract and it just start replicating right there. Immediately, it’s present and replicating in your lungs and in your nose. And that alone elicits enough of an immune response to cause us to feel really crappy and even cause us to feel disease.

But we could have just set the narrative better at the beginning: Look, you might get sick again, but your risk of landing in the hospital is going to be really low, and if you get a booster, you might still get sick again, but your risk of landing in the hospital is going to be even lower. That’s something I think humans can deal with, and I think the public could have understood it.

But it’s why we don’t see the severe disease as much, with a second exposure or an exposure after vaccination: For most people, it’s not getting into the heart and the liver and stuff nearly as easily.

But it doesn’t have to. It’s still causing symptomatic disease. And maybe mucosal vaccines could stop this, but without them we’re likely to continue seeing infections and even symptomatic infections.

through most of 2020 and into 2021, though. Back then, I think the conventional wisdom was that a single exposure — through infection or vaccination — would be the end of the pandemic for you. If this is basic virology and immunology, how did we get that so wrong?

The short answer is that epidemiologists are not immunologists and immunologists are not virologists and virologists are not epidemiologists. And, in general, physicians don’t know anything about the details.

But this failure had some pretty concrete impacts. When reinfections first began popping up, people were surprised, they were scared, and then, to some degree, they lost trust in vaccines. And the people they were turning to for guidance — not only did they not warn us about that, they were slow to acknowledge it, as well.

It had dramatic impacts and ripple effects that will last for years to limit our ability to get populations properly vaccinated.

the worst thing we can do during a pandemic is set inappropriately high expectations. These vaccines are incredible, they’ve had an enormously positive impact on mortality, but they were never going to end the pandemic.

And now, there’s a huge number of people questioning, do these vaccines even do anything?

For babies born today, though, I really think they’re not going to view Covid as any different than other viruses. By the time they are 20, it will be like any other virus to them. Because their immune systems will have grown up with it.

Instead, we set society up for failure, since people feel like the government failed everyone, that biology failed us, and that this was a crazy virus that has broken all the rules of our immune system, when it’s just doing what we’ve always known it would do.

How do you wish we had messaged things differently? What would it have meant to communicate early and clearly that Covid was a textbook virus, as you say?I think the biggest thing would have been just to say, we understand the enemy.

To say that this is a textbook virus, it doesn’t mean that it’s not killing people. Objectively, it’s still killing more people than any other infectious disease

What it means is that we could’ve taken action based on what we knew, rather than waiting around to prove everything and publish papers in Nature and Science talking about things we already knew.

We could have prepared for November and December of 2020 and then for November and December of 2021. But everyone kept saying, we don’t know if it’s going to come back. We knew it was going to come back and it makes me want to cry to think about it. We did nothing and hundreds of thousands of people died. We didn’t prepare nursing homes because we all got to the summer of 2020 and we said, cross our fingers.

We knew how tests worked. We knew about serial testing and why it was important for a public health approach. We knew that vaccines could have really good impacts once they were around. And if you were looking through the correct lens, we even knew that they weren’t going to stop transmission.

We didn’t have to live in a world where we were flying blind. We could have lived in a world where we’re knowledgeable. But instead, we chose almost across the board to will ourselves into this state of fear and anxiety.

And that really started in the earliest days. Almost the first experience I had was a lot like that movie with Jennifer Lawrence —Don’t Look Up.

none of this was complicated. You just had to ask a simple question: what would happen if you took away all immunity from an adult? Well, once you control for no immunity, adults are going to get very, very sick.

Of course, by and large, babies didn’t get very sick from this disease.Babies are immunologically naïve, but they are also resilient. A virus can tear up a baby, but a baby can repair its tissue so fast. Adults don’t have that. It’s just like a baby getting a cut. They’ll heal really quick

An adult getting a cut — you go by age, and every decade of age that you are, it’s going to take exponentially longer for that wound to heal. Eventually get to 80 or 90 and the wound can’t even heal. In the immunology world, this is called “tolerance.”

why are all these organ systems getting damaged when other viruses don’t seem to do that? It’s natural to think, it’s Covid — this is a weird disease. But it’s much more a story about immunity and how it develops than about the virus or the disease. None of our organ systems had any immune defenses around to help them out. And I think that the majority of post-acute sequelae and multi-organ complications and long Covid — they are not the result of the virus being a crazy different virus, but are a result of this virus replicating in an environment where there were such absent or exceedingly low defenses.

Is it the same whenever we encounter a virus for the first time?Think about travelers. Travelers get way more sick from a local disease than people who grew up with that virus. If you get malaria as a traveler, you’re much more likely to get really sick. You don’t see everyone in Nicaragua taking chloroquine every day. But you definitely see travelers taking it, because malaria can be deadly for adults.

What about, not severity, but post-acute complications — do we have long malaria? Do we have liver complications from dengue?

The really hard part of answering that question is there’s just not enough data on the frequency of long-term effects, because nothing like this has ever happened at such scale. It’s like everyone in Europe and North America suddenly traveled to a country where malaria was endemic.

Or think about H.I.V. It essentially kills your immune system, and once the immune barriers are down, other viruses that used to infect humans would get into tissues that we didn’t like them to get into. If there wasn’t such a clear signal of a loss of CD-4 T cells to explain it, people might still be scratching their heads and going, man, I wonder why all these patients are getting fungal infections. Well, there’s a virus there that’s depleting their immune system.

Covid is absolutely waking the world up to this — to the fact that there are really weird long-term sequelae to viruses when they infect organ systems that would normally be protected. And I think we’re going to find that more and more cancers are being attributed to viral infections.

It wasn’t that long ago that we first learned that most cases of cervical cancer were caused by H.P.V. — I think the 1980s. And now we have a vaccine for H.P.V. and rates of cervical cancer have fallen by two-thirds.

what about incidence? We’ve talked at a few points about how important it is to think about all of these questions in terms of the scale. What is the right scale for thinking about future long Covid, for instance, or other post-acute sequelae?

I think the absolute risk, per infection, is going down and down and down. That’s just true.

he U.K.’s Office of National Statistics, which shows a much lower risk of developing long Covid now, from reinfection, than from an initial infection earlier in the pandemic.

the worst is definitely behind us, which is a good thing, especially for people who worry that the problems will keep building and a lot of people — or even everyone — will get long Covid symptoms. I don’t think there’s a world where we’re looking at the babies of today dealing with long Covid at any meaningful scale.

a lot of the fear right now comes from the worst cases, and there’s a lot of worst cases. Even one of the people that I know well, I know in their mind they’re worried that they’ll never recover, but I think objectively they are recovering slowly. It might not be an eight month course. It might be a year and a half. But they will get better. Most of us will.

Also, college students aren’t full-fledged grown-ups. They do need guidance, and they benefit from it.

“The campus protests of the late 1960s sought in part to dismantle the in loco parentis role that colleges and universities had held in American life. But the past two decades have been shaped by a reversal of that, as institutions have sought to reconstruct this role in response to what students and parents paying enormous sums for their education have seemed to want.”

For us professors, the surrogate-parent paradigm means regular emails and other reminders from administrators that we should be taking our students’ temperatures, watching for glimmers of distress, intervening proactively and fashioning accommodations, especially if there has been some potentially discomfiting global, national or local news event

where does reasonable consideration end and unreasonable coddling begin? And what do validation and comfort have to do with learning?

Arguably, everything. If you’re not mentally healthy, you’ll be harder pressed to do the reading, writing and critical thinking at the core of college work

many schools have encouraged that mind-set, casting themselves as stewards of students’ welfare, guarantors of their safety, places of refuge, precincts of healing.

But are we responsibly preparing them for the world after college — and for the independence, toughness, resourcefulness and resilience it will almost surely demand of them — when we too easily dole out A’s, too readily grant extensions, too gingerly deliver critiques, and too quickly wonder and sound alarms about any disturbance in the atmosphere?

We keep witnessing episodes of students taking their colleges to task in ways that smack of entitlement and fragility and are out of bounds

Hamline’s president, Fayneese Miller, defended that sequence of events by saying that to not weigh academic freedom against a “debt to the traditions, beliefs and views of students” is a “privileged reaction.”

That’s a troubling assertion, as Tom Nichols wrote in The Atlantic: “If you don’t want your traditions, beliefs or views challenged, then don’t come to a university, at least not to study anything in the humanities or the social sciences.”

The school is a merchant, a kind of department store, or so a student could easily assume, based on the come-ons from affluent colleges competing with one another for applicants. They peddle tantalizing dining options, themed living arrangements, diverse amusements. They assign students the role of discerning customers. And the customer is always right.

But in an educational environment, that credo is all wrong, because learning means occasionally being provoked, frequently being unsettled and regularly being yanked outside of your comfort zone,

most students can handle that dislocation — if they’re properly prepped for it, if they’re made to understand its benefits. We shortchange them when we sell them short

starting in the mid-2010s, those beloved blue skies began to disappear. First, the smoke came in occasional bursts, from wildfires in Canada or California or Siberia, and blew away when the wind changed direction. Within a few summers, though, it was coming in thicker, from more directions at once, and lasting longer.

Sometimes there were weeks when you were advised not to open your windows or exercise outside. Sometimes there were long stretches where you weren’t supposed to breathe the outside air at all.

Now lots of Bryant’s clients wanted to talk about climate change. They wanted to talk about how strange and disorienting and scary this new reality felt, about what the future might be like and how they might face it, about how to deal with all the strong feelings — helplessness, rage, depression, guilt — being stirred up inside them.

As a therapist, Bryant found himself unsure how to respond

while his clinical education offered lots of training in, say, substance abuse or family therapy, there was nothing about environmental crisis, or how to treat patients whose mental health was affected by it

Bryant immersed himself in the subject, joining and founding associations of climate-concerned therapists

eventually started a website, Climate & Mind, to serve as a sort of clearing house for other therapists searching for resources. Instead, the site became an unexpected window into the experience of would-be patients: Bryant found himself receiving messages from people around the world who stumbled across it while looking for help.

Over and over, he read the same story, of potential patients who’d gone looking for someone to talk to about climate change and other environmental crises, only to be told that they were overreacting — that their concern, and not the climate, was what was out of whack and in need of treatment.

“You come in and talk about how anxious you are that fossil-fuel companies continue to pump CO2 into the air, and your therapist says, ‘So, tell me about your mother.’”

In many of the messages, people asked Bryant for referrals to climate-focused therapists in Houston or Canada or Taiwan, wherever it was the writer lived.

his practice had shifted to reflect a new reality of climate psychology. His clients didn’t just bring up the changing climate incidentally, or during disconcerting local reminders; rather, many were activists or scientists or people who specifically sought out Bryant because of their concerns about the climate crisis.

could now turn to resources like the list maintained by the Climate Psychology Alliance North America, which contains more than 100 psychotherapists around the country who are what the organization calls “climate aware.”

But treating those fears also stirred up lots of complicated questions that no one was quite sure how to answer. The traditional focus of his field, Bryant said, could be oversimplified as “fixing the individual”: treating patients as separate entities working on their personal growth

It had been a challenging few years, Bryant told me when I first called to talk about his work. There were some ways in which climate fears were a natural fit in the therapy room, and he believed the field had coalesced around some answers that felt clear and useful

Climate change, by contrast, was a species-wide problem, a profound and constant reminder of how deeply intertwined we all are in complex systems — atmospheric, biospheric, economic — that are much bigger than us. It sometimes felt like a direct challenge to old therapeutic paradigms — and perhaps a chance to replace them with something better.

In one of climate psychology’s founding papers, published in 2011, Susan Clayton and Thomas J. Doherty posited that climate change would have “significant negative effects on mental health and well-being.” They described three broad types of possible impacts: the acute trauma of living through climate disasters; the corroding fear of a collapsing future; and the psychosocial decay that could damage the fabric of communities dealing with disruptive changes

All of these, they wrote, would make the climate crisis “as much a psychological and social phenomenon as a matter of biodiversity and geophysics.”

Many of these predictions have since been borne out

Studies have found rates of PTSD spiking in the wake of disasters, and in 2017 the American Psychological Association defined “ecoanxiety” as “a chronic fear of environmental doom.”

Climate-driven migration is on the rise, and so are stories of xenophobia and community mistrust.

According to a 2022 survey by Yale and George Mason University, a majority of Americans report that they spend time worrying about climate change.

Many say it has led to symptoms of depression or anxiety; more than a quarter make an active effort not to think about it.

There was little or no attention to the fact that living through, or helping to cause, a collapse of nature can also be mentally harmful.

In June, the Yale Journal of Biology and Medicine published a paper cautioning that the world at large was facing “a psychological condition of ‘systemic uncertainty,’” in which “difficult emotions arise not only from experiencing the ecological loss itself,” but also from the fact that our lives are inescapably embedded in systems that keep on making those losses worse.

Climate change, in other words, surrounds us with constant reminders of “ethical dilemmas and deep social criticism of modern society. In its essence, climate crisis questions the relationship of humans with nature and the meaning of being human in the Anthropocene.”

This is not an easy way to live.

Living within a context that is obviously unhealthful, he wrote, is painful: “a dimly intuited ‘fall’ from which we spend our lives trying to recover, a guilt we can never quite grasp or expiate” — a feeling of loss or dislocation whose true origins we look for, but often fail to see. This confusion leaves us feeling even worse.

When Barbara Easterlin first started studying environmental psychology 30 years ago, she told me, the focus of study was on ways in which cultivating a relationship with nature can be good for mental health

A poll by the American Psychiatric Association in the same year found that nearly half of Americans think climate change is already harming the nation’s mental health.

the field is still so new that it does not yet have evidence-tested treatments or standards of practice. Therapists sometimes feel as if they are finding the path as they go.

Rebecca Weston, a licensed clinical social worker practicing in New York and a co-president of the CPA-NA, told me that when she treats anxiety disorders, her goal is often to help the patient understand how much of their fear is internally produced — out of proportion to the reality they’re facing

climate anxiety is a different challenge, because people worried about climate change and environmental breakdown are often having the opposite experience: Their worries are rational and evidence-based, but they feel isolated and frustrated because they’re living in a society that tends to dismiss them.

One of the emerging tenets of climate psychology is that counselors should validate their clients’ climate-related emotions as reasonable, not pathological

it does mean validating that feelings like grief and fear and shame aren’t a form of sickness, but, as Weston put it, “are actually rational responses to a world that’s very scary and very uncertain and very dangerous for people

In the words of a handbook on climate psychology, “Paying heed to what is happening in our communities and across the globe is a healthier response than turning away in denial or disavowal.”

But this, too, raises difficult questions. “How much do we normalize people to the system we’re in?” Weston asked. “And is that the definition of health?

Or is the definition of health resisting the things that are making us so unhappy? That’s the profound tension within our field.”

“It seems to shift all the time, the sort of content and material that people are bringing in,” Alexandra Woollacott, a psychotherapist in Seattle, told the group. Sometimes it was a pervasive anxiety about the future, or trauma responses to fires or smoke or heat; other times, clients, especially young ones, wanted to vent their “sort of righteous anger and sense of betrayal” at the various powers that had built and maintained a society that was so destructive.

“I’m so glad that we have each other to process this,” she said, “because we’re humans living through this, too. I have my own trauma responses to it, I have my own grief process around it, I have my own fury at government and oil companies, and I think I don’t want to burden my clients with my own emotional response to it.”

In a field that has long emphasized boundaries, discouraging therapists from bringing their own issues or experiences into the therapy room, climate therapy offers a particular challenge: Separation can be harder when the problems at hand affect therapist and client alike

Some therapists I spoke to were worried about navigating the breakdown of barriers, while others had embraced it. “There is no place on the planet that won’t eventually be impacted, where client and therapist won’t be in it together,” a family therapist wrote in a CPA-NA newsletter. “Most therapists I know have become more vulnerable and self-disclosing in their practice.”

“If you look at or consider typical theoretical framings of something like post-traumatic growth, which is the understanding of this idea that people can sort of grow and become stronger and better after a traumatic event,” she said, then the climate crisis poses a dilemma because “there is no afterwards, right? There is no resolution anytime in our lifetimes to this crisis that we nonetheless have to build the capacities to face and to endure and to hopefully engage.”

many of her patients are also disconnected from the natural world, which means that they struggle to process or even recognize the grief and alienation that comes from living in a society that treats nature as other, a resource to be used and discarded.

“How,” she asked, “do you think about resilience apart from resolution?”

she believed this framing reflected and reinforced a bias inherent in a field that has long been most accessible to, and practiced by, the privileged. It was hardly new in the world, after all, to face the collapse of your entire way of life and still find ways to keep going.

Torres said that she sometimes takes her therapy sessions outside or asks patients to remember their earliest and deepest connections with animals or plants or places. She believes it will help if they learn to think of themselves “as rooted beings that aren’t just simply living in the human overlay on the environment.” It was valuable to recognize, she said, that “we are part of the land” and suffer when it suffers.

Torres described introducing her clients to methods — mindfulness, distress tolerance, emotion regulation — to help them manage acute feelings of stress or panic and to avoid the brittleness of burnout.

She also encourages them to narrativize the problem, including themselves as agents of change inside stories about how they came to be in this situation, and how they might make it different.

then she encourages them to find a community of other people who care about the same problems, with whom they could connect outside the therapy room. As Woollacott said earlier: “People who share your values. People who are committed to not looking away.”

Dwyer told the group that she had been thinking more about psychological adaptation as a form of climate mitigation

Therapy, she said, could be a way to steward human energy and creative capacities at a time when they’re most needed.

It was hard, Bryant told me when we first spoke, to do this sort of work without finding yourself asking bigger questions — namely, what was therapy actually about?

Many of the therapists I talked to spoke of their role not as “fixing” a patient’s problem or responding to a pathology, but simply giving their patients the tools to name and explore their most difficult emotions, to sit with painful feelings without instantly running away from them

many of the methods in their traditional tool kits continue to be useful in climate psychology. Anxiety and hopelessness and anger are all familiar territory, after all, with long histories of well-studied treatments.

They focused on trying to help patients develop coping skills and find meaning amid destabilization, to still see themselves as having agency and choice.

Weston, the therapist in New York, has had patients who struggle to be in a world that surrounds them with waste and trash, who experience panic because they can never find a place free of reminders of their society’s destruction

eston said, that she has trouble with the repeated refrain that therapist and patient experiencing the same losses and dreads at the same time constituted a major departure from traditional therapeutic practice

“I’m so excited by what you’re bringing in,” Woollacott replied. “I’m doing psychoanalytic training at the moment, and we study attachment theory” — how the stability of early emotional bonds affects future relationships and feelings of well-being. “But nowhere in the literature does it talk about our attachment to the land.”

Lately, Bryant told me, he’s been most excited about the work that happens outside the therapy room: places where groups of people gather to talk about their feelings and the future they’re facing

It was at such a meeting — a community event where people were brainstorming ways to adapt to climate chaos — that Weston, realizing she had concrete skills to offer, was inspired to rework her practice to focus on the challenge. She remembers finding the gathering empowering and energizing in a way she hadn’t experienced before. In such settings, it was automatic that people would feel embraced instead of isolated, natural that the conversation would start moving away from the individual and toward collective experiences and ideas.

There was no fully separate space, to be mended on its own. There was only a shared and broken world, and a community united in loving it.

The tensions have risen as Ford and other global automakers have spent billions of dollars designing and building EVs, a move that looked especially smart a year ago when they were caught off guard by the strong demand for their new offerings. 

This past week, General Motors said it would delay opening a large EV truck factory in Michigan by a year, citing a need “to better manage capital investments while aligning with evolving EV demand.” The move followed an earlier announcement by Ford pushing back to late 2024 a target of building 600,000 EVs annually. The company has also temporarily cut one of the production shifts for its electric pickup and paused construction of a $3.5 billion battery plant in Michigan. 

In the U.S., for every five Democrats owning an EV there are two Republicans, said Alexander Edwards, president of Strategic Vision, which surveys new-vehicle buyers. 

His data finds that Democrats give priority to “environmentally friendly” when buying their cars while Republicans have other things they are looking for, such as performance and prestige.

On the campaign trail, however, EVs don’t always sound so cool. The GOP presidential hopeful Vivek Ramaswamy, who is against subsidies, has drawn laughs as he suggests that EV buyers are motivated by “a psychological insecurity,” while former Vice President Mike Pence said during the second Republican presidential primary debate that Biden’s efforts “are driving American gasoline, automotive manufacturing, into the graveyard.”  

As the Democrat talks about trying to protect automotive jobs and help the environment with green technology, they raise concerns about losing work and question whether the governments should subsidize them or mandate future zero-emission vehicle sales, as California has done.  

“The real question is whether we’ll lead or we’ll fall behind in the race to the future; or whether we’ll build these vehicles and the batteries that go in them here in the United States or rely on other countries,” Biden said while visiting a Ford factory early in his administration. 

Underpinning the politics of EVs is an economic divide, made more stark by the rise of interest rates. Most EVs are more expensive than the average new vehicle—which sold for about $46,000 in September.

As new cars and trucks become more costly, the practical effect on buyers shows up in Strategic Vision’s survey: The median family household income of new-car buyers has risen to $122,000. That is a significant increase from around $90,000, where it had been at for a couple of decades until just recently. EV buyers are even better off, with a median household income of $186,000.

In some ways, the green car tensions are a return to the 2012 political season, when GM’s Chevrolet Volt became the embodiment of the Obama administration’s rescue of the Detroit auto industry in 2009 and efforts to promote electrified vehicles.

Former House Speaker Newt Gingrich, who unsuccessfully sought the Republican presidential nomination, said the problem with the “Obama car” was that one couldn’t put a gun rack in the plug-in hybrid vehicle.

Sales of the Volt disappointed, and Dan Akerson, then CEO of GM, was left fuming that the company hadn’t designed the sedan to become “a political punching bag.”

GM later killed off the Volt.

the real problem with Mr. Andreessen’s manifesto may be not that it’s too outlandish, but that it’s too on-the-nose.

Neoreactionary thought contends that the world would operate much better in the hands of a few tech-savvy elites in a quasi-feudal system. Mr. Andreessen, through this lens, believes that advancing technology is the most virtuous thing one can do.

And the way we regard that wealth as a product of good decision-making and righteous hard work, no matter how many billions of dollars of investors’ money they may have vaporized, how many other people contributed to their success or how much government money subsidized it

In the case of ordinary individuals, however, debt is regarded as not just a financial failure but a moral one. (If you are successful and have paid your student loans off, taking them out in the first place was a good decision. If you haven’t and can’t, you were irresponsible and the government should not enable your freeloading.)

He articulates (albeit in a refrigerator magnet poetry kind of way) a strain of nihilism that has gained traction among tech elites, and reveals much of how they think about their few remaining responsibilities to society.

This strain of thinking is disdainful of democracy and opposes institutions (a free press, for example) that bolster it. It despises egalitarianism and views oppression of marginalized groups as a problem of their own making.

Who is doing the telling here, and who is being told? It’s not technology (a term so broad it encompasses almost everything) that’s reducing wages and increasing inequality — it’s the ultrawealthy, people like Mr. Andreessen.

It argues for an extreme acceleration of technological advancement regardless of consequences, in a way that makes “move fast and break things” seem modest.

Mr. Andreessen claims to be against authoritarianism, but really, it’s a matter of choosing the authoritarian — and the neoreactionary authoritarian of choice is a C.E.O. who operates as king.

it is taken seriously by people who imagine themselves potential Chief Executive Authoritarians, or at the very least proxies. This includes another Silicon Valley billionaire, Peter Thiel, who has funded some of Mr. Yarvin’s work and once wrote that he believed democracy and freedom were incompatible.

how did they sell so many other people on it? By pretending that for all their wealth and influence, they are not the real elites.

When Mr. Andreessen says “we” are being lied to, he includes himself, and when he names the liars, they’re those in “the ivory tower, the know-it-all credentialed expert worldview,” who are “disconnected from the real world, delusional, unelected, and unaccountable — playing God with everyone else’s lives, with total insulation from the consequences.”

His depiction of academics of course sounds a lot like — well, like tech overlords, who are often insulated from the real-world consequences of their inventions, including but not limited to promoting disinformation, facilitating fraud and enabling genocidal regimes.

“We are told that technology takes our jobs,” Mr. Andreessen writes, “reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.”

You can see it in the way we valorize the C.E.O.s of “unicorn” companies who have expanded their wealth far beyond what could possibly be justified by their individual contributions

The argument for total acceleration of technological development is not about optimism, except in the sense that the Andreessens and Thiels and Musks are certain that they will succeed. It’s pessimism about democracy — and ultimately, humanity.

the billionaire classes of Silicon Valley are frustrated that they cannot just accelerate their way into the future, one in which they can become human/technological hybrids and live forever in a colony on Mars

In pursuit of this accelerated post-Singularity future, any harm they’ve done to the planet or to other people is necessary collateral damage. It’s the delusion of people who’ve been able to buy their way out of everything uncomfortable, inconvenient or painful, and don’t accept the fact that they cannot buy their way out of death.

A higher proportion of young people also believe that urgent action is required to deal with climate change.

nearly 90 per cent of the over-65s say they recycle as much as possible compared with only half of young people.

Retired Britons are also three times more likely than younger people to have installed energy-saving measure

The over-65s are also more likely to have reduced the number of flights they take, to take steps to save water, to repair items, to wash clothes at low temperatures and to choose a renewable energy tariff.

They are also more likely to turn the heating down and switch off lights in rooms they are not using.

young people have a long way to go to live up to their reputation as being concerned for the planet.

There are some areas where 18 to 24-year-olds are doing more than older people, however. Young people are more likely to walk or cycle than older people

and they are more likely to be vegetarian or vegan.

17 per cent of 18 to 24-year-olds do not eat any meat compared with only 8 per cent of the over-65s

cross the population as a whole, 12 per cent of people say they are vegan or vegetarian.

Half of people questioned said that dealing with the cost of living is a greater priority than making changes to lower their environmental impact.

If OnlyFans’s creator earnings were taken as a whole, the company would rank around No. 90 on Forbes’s list of the biggest private companies in America by revenue, ahead of Twitter (now called X), Neiman Marcus Group, New Balance, Hard Rock International and Hallmark Cards.

Many creators now operate like independent media companies, with support staffs, growth strategies and promotional budgets, and work to apply the cold quantification and data analytics of online marketing to the creation of a fantasy life.

The subscription site has often been laughed off as a tabloid punchline, a bawdy corner of the internet where young, underpaid women (teachers, nurses, cops) sell nude photos, get found out and lose their jobs.

pressures to perform for a global audience; an internet that never forgets. “There is simply no room for naivety,” one said in a guide posted to Reddit’s r/CreatorsAdvice.

America’s social media giants for years have held up online virality as the ultimate goal, doling out measurements of followers, reactions and hearts with an unspoken promise: that internet love can translate into sponsorships and endorsement deals

But OnlyFans represents the creator economy at its most blatantly transactional — a place where viewers pay upfront for creators’ labor, and intimacy is just another unit of content to monetize.

The fast ascent of OnlyFans further spotlights how the internet has helped foster a new style of modern gig work that creators see as safe, remote and self-directed,

Creators’ nonchalance about the digital sex trade has fueled a broader debate about whether the site’s promotion of feminist autonomy is a facade: just a new class of techno-capitalism, selling the same patriarchal dream.

But OnlyFans increasingly has become the model for how a new generation of online creators gets paid. Influencers popular on mainstream sites use it to capitalize on the audiences they’ve spent years building. And OnlyFans creators have turned going viral on the big social networks into a marketing strategy, using Facebook, Twitter and TikTok as sales funnels for getting new viewers to subscribe.

many creators, she added, still find it uniquely alluring — a rational choice in an often-irrational environment for gender, work and power. “Why would I spend my day doing dirty, degrading, minimum-wage labor when I can do something that brings more money in and that I have a lot more control over?”

it is targeting major “growth regions” in Latin America, Europe and Australia. (The Mexican diver Diego Balleza said he is using his $15-a-month account to save up for next year’s Paris Olympics.)

“Does an accountant always enjoy their work? No. All work has pleasure and pain, and a lot of it is boring and annoying. Does that mean they’re being exploited?”

Adams’s operation is registered in state business records as a limited liability company and offers quarterly employee performance reviews and catered lunch. It also runs with factory-like efficiency, thanks largely to a system designed in-house to track millions of data points on customers and content and ensure every video is rigorously planned and optimized.

Since sending her first photo in 2021, Adams’s OnlyFans accounts have earned $16.5 million in sales, more than 1.4 million fans and more than 11 million “likes.” She now makes about $30,000 a day — more than most American small businesses — from subscriptions, video sales, messages and tips, half of which is pure profit

Adams’s team sees its business as one of harmless, destigmatized gratification, in which both sides get what they want. The buyers are swiped over in dating apps, widowed, divorced or bored, eager to pay for the illusion of intimacy with an otherwise unattainable match. And the sellers see themselves as not all that different from the influencers they watched growing up on YouTube, charging for parts of their lives they’d otherwise share for free.

“This is normal for my generation, you know?

“I can go on TikTok right now and see ten girls wearing the bare minimum of clothing just to get people to join their page. Why not go the extra step to make money off it?”

the job can be financially precarious and mentally taxing, demanding not just the technical labor of recording, editing, managing and marketing but also the physical and emotional labor of adopting a persona to keep clients feeling special and eager to spend.

enix International Limited, is based, the company said its sales grew from $238 million in 2019 to more than $5.5 billion last year.

Its international army of creators has also grown from 348,000 in 2019 to more than 3 million today — a tenfold increase.

The company paid its owner, the Ukrainian American venture capitalist Leonid Radvinsky, $338 million in dividends last year.)

portion of its creator base and 70 percent of its annual revenue

When Tim Stokely, a London-based operator of live-cam sex sites, founded OnlyFans with his brother in 2016, he framed it as a simple way to monetize the creators who were becoming the world’s new celebrities — the same online influencers, just with a payment button. In 2019, Stokely told Wired magazine that his site was like “a bolt-on to your existing social media,” in the same way “Uber is a bolt-on to your car.”

Before OnlyFans, pornography on the internet had been largely a top-down enterprise, with agents, producers, studios and other middlemen hoarding the profits of performers’ work. OnlyFans democratized that business model, letting the workers run the show: recording their own content, deciding their prices, selling it however they’d like and reaping the full reward.

The platform bans real-world prostitution, as well as extreme or illegal content, and requires everyone who shows up on camera to verify they’re 18 or older by sending in a video selfie showing them holding a government-issued ID.

OnlyFans operates as a neutral marketplace, with no ads, trending topics or recommendation algorithms, placing few limitations on what creators can sell but also making it necessary for them to market themselves or fade away.

After sending other creators’ agents their money over PayPal, Adams’s ad workers send suggestions over the messaging app Telegram on how Bryce should be marketed, depending on the clientele. OnlyFans models whose fans tend to prefer the “girlfriend experience,” for instance, are told to talk up her authenticity: “Bryce is a real, fit girl who wants to get to know you

Like most platforms, OnlyFans suffers from a problem of incredible pay inequality, with the bulk of the profits concentrated in the bank accounts of the lucky few.

the top 1 percent of accounts made 33 percent of the money, and that most accounts took home less than $145 a month

Watching their partner have sex with someone else sometimes sparked what they called “classic little jealousy issues,” which Adams said they resolved with “more communication, more growing up.” The money was just too good. And over time, they adopted a self-affirming ideology that framed everything as just business. Things that were tough to do but got easier with practice, like shooting a sex scene, they called, in gym terms, “reps.” Things one may not want to do at first, but require some mental work to approach, became “self-limiting beliefs.”

They started hiring workers through friends and family, and what was once just Adams became a team effort, in which everyone was expected to workshop caption and video ideas. The group evaluated content under what Brian, who is 31, called a “triangulation method” that factored their comfort level with a piece of content alongside its engagement potential and “brand match.” Bryce the person gave way to Bryce the brand, a commercialized persona drafted by committee and refined for maximum marketability.

One of the operation’s most subtly critical components is a piece of software known as “the Tool,” which they developed and maintain in-house. The Tool scrapes and compiles every “like” and view on all of Adams’s social network accounts, every OnlyFans “fan action” and transaction, and every text, sext and chat message — more than 20 million lines of text so far.

It houses reams of customer data and a library of preset messages that Adams and her chatters can send to fans, helping to automate their reactions and flirtations — “an 80 percent template for a personalized response,” she said.

And it’s linked to a searchable database, in which hundreds of sex scenes are described in detail — by price, total sales, participants and general theme — and given a unique “stock keeping unit,” or SKU, much like the scannable codes on a grocery store shelf. If a fan says they like a certain sexual scenario, a team member can instantly surface any relevant scenes for an easy upsell. “Classic inventory chain,” Adams said.

The systemized database is especially handy for the young women of Adams’s chat team, known as the “girlfriends,” who work at a bench of laptops in the gym’s upper loft. The Tool helped “supercharge her messaging, which ended up, like, 3X-ing her output,” Brian said, meaning it tripled.

Keeping men talking is especially important because the chat window is where Adams’s team sends out their mass-message sales promotions, and the girlfriends never really know what to expect. One girlfriend said she’s had as many as four different sexting sessions going at once.

Adams employs a small team that helps her pay other OnlyFans creators to give away codes fans can use for free short-term trials. The team tracks redemption rates and promotional effectiveness in a voluminous spreadsheet, looking for guys who double up on discount codes, known as “stackers,” as well as bad bets and outright fraud.

Many OnlyFans creators don’t offer anything explicit, and the site has pushed to spotlight its stable of chefs, comedians and mountain bikers on a streaming channel, OFTV. But erotic content on the platform is inescapable; even some outwardly conventional creators shed their clothes behind the paywall

Creators with a more hardcore fan base, meanwhile, are told to cut to the chase: “300+ sex tapes & counting”; “Bryce doesn’t say no, she’s the most wild, authentic girl you will ever find.”

The $18 an hour she makes on the ad team, however, is increasingly dwarfed by the money Leigh makes from her personal OnlyFans account, where she sells sex scenes with her boyfriend for $10 a month. Leigh made $92,000 in gross sales in July, thanks largely to revenue from new fans who found her through Adams or the bikini videos Leigh posts to her 170,000-follower TikTok account

“This is a real job. You dedicate your time to it every single day. You’re always learning, you’re always doing new things,” she said. “I’d never thought I’d be good at business, but learning all these business tactics really empowers you. I have my own LLC; I don’t know any other 20-year-old right now that has their own LLC.”

The team is meeting all traffic goals, per their internal dashboard, which showed that through the day on a recent Thursday they’d gained 2,221,835 video plays, 19,707 landing-page clicks, 6,372 new OnlyFans subscribers and 9,024 new social-network followers. And to keep in shape, Adams and her boyfriend are abiding by a rigorous daily diet and workout plan

They eat the same Chick-fil-A salad at every lunch, track every calorie and pay a gym assistant to record data on every rep and weight of their exercise.

But the OnlyFans business is competitive, and it does not always feel to the couple like they’ve done enough. Their new personal challenge, they said, is to go viral on the other platforms as often as possible, largely through jokey TikTok clips and bikini videos that don’t give away too much.

the host told creators this sales-funnel technique was key to helping build the “cult of you”: “Someone’s fascination will become infatuation, which will make you a lot of money.”

Adams’s company has worked to reverse engineer the often-inscrutable art of virality, and Brian now estimates Adams makes about $5,000 in revenue for every million short-form video views she gets on TikTok.

Her team has begun ranking each platform by the amount of money they expect they can get from each viewer there, a metric they call “fan lifetime value.” (Subscribers who click through to her from Facebook tend to spend the most, the data show. Facebook declined to comment.)

The younger workers said they see the couple as mentors, and the two are constantly reminding them that the job of a creator is not a “lottery ticket” and requires a persistent grind. Whenever one complains about their lack of engagement, Brian said he responds, “When’s the last time you posted 60 different videos, 60 days in a row, on your Instagram Reels?”

But some have taken to it quite naturally. Rayna Rose, 19, was working last year at a hair salon, sweeping floors for $12 an hour, when an old high school classmate who worked with Adams asked whether she wanted to try OnlyFans and make $500 a video.

Rose started making videos and working as a chatter for $18 an hour but recently renegotiated her contract with Adams to focus more on her personal OnlyFans account, where she has nearly 30,000 fans, many of whom pay $10 a month.

One recent evening this summer, Adams was in the farm’s gym when her boyfriend told her he was headed to their guest room to record a collab with Rose, who was wearing a blue bikini top and braided pigtails.

“Go have fun,” Adams told them as they walked away. “Make good content.” The 15-minute video has so far sold more than 1,400 copies and accounted for more than $30,000 in sales.

Rose said she has lost friends due to her “lifestyle,” with one messaging her recently, “Can you imagine how successful you would be if you studied regularly and spent your time wisely?”

The message stung but, in Rose’s eyes, they didn’t understand her at all. She feels, for the first time, like she has a sense of purpose: She wants to be a full-time influencer. She expects to clear $200,000 in earnings this year and is now planning to move out of her parents’ house.

“I had no idea what I wanted to do with my life. And now I know,” she said. “I want to be big. I want to be, like, mainstream.”

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.