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Thanks to social-science research, we now know that children’s life chances are profoundly affected by their context—not only income and family circumstances but also their community—more so than by their genetic profile or the medical care they receive.

With this in mind, we created what we call the Index of Deep Disadvantage, which reflects two traditional measures of income (the poverty rate and the “deep poverty rate,” meaning those with incomes below half the poverty line), two markers of health (birth weight and life expectancy), and the rate of social mobility for children who grow up in low-income families. We used this index to rank the roughly 3,100 counties in the United States along with the 500 most populous cities.

Immediately, the rankings revealed a stark geographical pattern.

the most disadvantaged places on our index were primarily rural

Though some of these were majority white, most were majority Black or Hispanic.

these places resembled, well, colonies—internal colonies within the U.S.

Considerable poverty exists in Chicago, Los Angeles, and New York. But in our apples-to-apples comparison, none of those cities ranked among even the 600 most disadvantaged places in the nation

The only cities on that list were a relatively small number of industrial municipalities such as Cleveland, Detroit, and Rochester.

Those living in the 200 most disadvantaged places on our index were just as likely to have major difficulties paying for housing as those in America’s 500 largest cities.

The places that our index identified as the 200 most disadvantaged are concentrated in three regions—Appalachia, South Texas, and the southern Cotton Belt.

hese places share a history of intensive resource extraction and human exploitation not seen to the same degree elsewhere in the United States

In each place, this economic pattern emerged (or, in the case of the Cotton Belt, fully flourished) in the late 19th or early 20th century. In each place, one industry came to dominate the economy, a pattern that held, broadly, until the 1960s, when King Cotton, King Tobacco, King Coal, and South Texas agriculture, would bow to the twin forces of automation and global competition.

many places with large Native American populations ranked among the most disadvantaged in the nation

While visiting many of the nation’s most disadvantaged places, we set out to build on this work through historical research, ethnographic observations, and in-depth interviews.

For generations, the appearance of towns in South Texas followed a pattern of social hierarchy: sturdy wood-frame houses, paved streets, and enclosed sewers in the white neighborhoods; shacks, dirt roads, and privies in the Mexican parts of town. Forced to migrate to find work during the off-season, generations of Mexican American children lost their right to a decent education. Even today, adult-illiteracy rates in these places are among the highest in the nation.

in Leflore County, Mississippi, for example, Black residents told us that violence was the No. 1 problem they face. The rate of death due to interpersonal violence there was nearly four times the national average, and well above that of Cook County, Illinois, home to Chicago

Throughout these regions, we saw the same themes emerge again and again—unequal schooling, the collapse of social infrastructure, violence, entrenched public corruption, and structural racism embedded in government programs.

Overall, poverty rates in these places are very low, babies are born healthy, people live to a ripe old age, and a low-income child usually has a similar chance of making it into the middle class as any other kid.

er they were forcibly relocated onto land mostly inhospitable to human habit

hey have enjoyed the lowest rates of violent crime, income inequality, and public corruption in the nation. These counties are unusually rich in social capital: Residents are connected to one another through volunteerism, membership in civic organizations, and participation in other community activities.

Counties that rank among those of greatest advantage began as agricultural communities with modestly sized farms, many originally secured through the 1862 Homestead Act that made landownership widely available. Many of these places have built on this history of broad-based wealth by making significant investments in schools, which has contributed to high graduation and college enrollment rates over generations.

One cannot fully understand the benefits enjoyed in America’s most advantaged places without considering the historic (and ongoing) exploitation of migrant labor that has gone on in them, mostly drawn from the U.S. border regions.

these communities have been more successful than most in preventing poverty, promoting health, and ensuring a level playing field for their children.

The upper Midwest is also overwhelmingly white. When we examined the relationship between whiteness and rank on the index, we found that a higher percentage of white residents is a significant predictor of a place’s rank, which is not at all surprising when one considers that the good schools and the good jobs have long been bestowed liberally on whites while being denied to Hispanic and Black Americans.

What makes the communities that are most advantaged unique is their histories as places of broad-based wealth. How different would conditions be in other parts of the country had they followed a similar, equitable course?

The lesson is that people seem to thrive—not always in high salaries but in health and life chances—when inequality is low; when landownership is widespread; when social connection is high; and when corruption and violence are rare.

The social leveling that is characteristic of communities in the upper Midwest is more than just a quaint cultural feature. It is the foundation of a community’s well-being. Until these regions’ virtues are shared nationwide, poverty and disadvantage will continue to haunt America.

That requires a movement that takes innovation as seriously as it takes affordability.

what Bastani sees clearly is that the world we should want requires more than redistribution. It requires inventions and advances that render old problems obsolete and new possibilities manifold.

the authors urge conservatives to tackle costs directly. Too often, Republican proposals to cut government spending are just shell games that shift costs onto individuals. The conservative enthusiasm for moving Medicare beneficiaries onto (often more expensive!) private plans “risks being little more than an accounting trick — a purely nominal change in ‘who pays’ that would do little to address the underlying sources of cost growth.”

For now, though, it’s Democrats who are starting to take supply-side concerns seriously.

A problem of our era is there’s too little utopian thinking, but one worthy exception is Aaron Bastani’s “Fully Automated Luxury Communism,” a leftist tract that puts the technologies in development right now — artificial intelligence, renewable energy, asteroid mining, plant- and cell-based meats, and genetic editing — at the center of a postwork, postscarcity vision.

“What if everything could change?” he asks. “What if, more than simply meeting the great challenges of our time — from climate change to inequality and aging — we went far beyond them, putting today’s problems behind us like we did before with large predators and, for the most part, illness? What if, rather than having no sense of a different future, we decided history hadn’t actually begun?”

There are sharp limits on supply in all of these sectors because regulators make it hard to increase supply (zoning laws make it difficult to build housing), training and hiring workers is expensive (adding classrooms means adding teachers and teacher aides, and expanding health insurance requires more doctors and nurses) or both. “This can result in a vicious cycle in which subsidies for supply-constrained goods or services merely push up prices, necessitating greater subsidies, which then push up prices, ad infinitum,” they write.

Progressives have long known to look for problems on the demand side of the economy — to ask whether there are goods and services people need that they cannot afford. That will make today fairer, but to ensure tomorrow is radically better, we need to look for the choke points in the future we imagine, the places where the economy can’t or won’t supply the things we need. And then we need to fix them.

In a world where two-thirds of emissions now come from middle-income countries like China and India, the only way for humanity to both address climate change and poverty is to invent our way to clean energy that is plentiful and cheap and then spend enough to rapidly deploy it.

It is true that European countries free-ride off the high cost we pay for drugs, because it’s the U.S. market that drives innovation. But that doesn’t mean we’d be better off paying their prices, if that meant new drug development slowed. We don’t just want everyone to have health insurance in the future. We want them to be healthier, freed from diseases and pain that even the best health insurance today cannot cure or ease.

It’s ludicrous to say that the pharmaceutical system we have now is oriented toward innovation. It’s oriented toward profit; sometimes that intersects with innovation and sometimes it doesn’t.

We should combine price controls with new policies to encourage drug development. That could include everything from more funding of basic research to huge prizes for discovering drugs that treat particular conditions to more public funding for drug trials.

Years ago, Bernie Sanders had an interesting proposal for creating a system of pharmaceutical prizes in which companies could make millions or billions for inventing drugs that cured certain conditions, and those drugs would be immediately released without exclusive patent protections. Focusing on the need to make new drugs affordable while ignoring the need to make more of them exist is like trimming a garden you’ve stopped watering.

this is a lesson progressives are, increasingly, learning. This is clearest on climate. Much of the spending in the Biden agenda is dedicated to increasing the supply of renewable energy and advanced batteries while building the supply of carbon-neutral transportation options.

In a blog post, Jared Bernstein, a member of President Biden’s Council of Economic Advisers, and Ernie Tedeschi, a senior policy economist for the council, framed the Biden agenda as “an antidote for inflationary pressure” because much of it expands the long-term supply of the economy.

Climate change is the most pressing example

look closely and you can see something new and overdue emerging in American politics: supply-side progressivism.

I saw these gym protests as a similar idea: my body is my temple. What I’m doing here is my protection; I’m keeping myself strong. I’m building up my immune system, my body is my force field against whatever is coming.

The parts of listening to Bannon that were most destabilizing were when I heard him saying things that sounded like the left, and when I heard him saying things that I agreed with in part – not in whole, but where I saw that kernel of truth and I realized how effective it was going to be in the mix and match with what I see as a fascist project that he’s engaged in.

I expect Steve Bannon to be monstrous on immigration, on gender. I expect that from him. It’s when he’s talking about corporate control of the media and saying things that are true about big tech that I start to get queasy and ask, wait a minute, why is he saying more about this than a lot of people on the liberal side of the spectrum? Have we ceded this territory?

This point seems central. The mirror world isn’t devoid of truth. Instead, it’s destabilizing because elements of truth are there, but warped.

Absolutely. And the destabilizing piece is not simply that they’re saying something true. It’s when you realize people [on the left] have stopped saying that true thing. That’s when you realize that it has power.

If we were building multiracial, intergenerational social movements that were really rooted in confronting corporate power, then they could say whatever they want and it wouldn’t really bother me. But we’re talking about it less, and the more [conspiracists] talk about it, the more reticent we become. So it’s a dialectic that makes me queasy.

Ehrenreich has a completely different theory, which I think is much more plausible, which is this is the 1980s: people are in the wreckage of the failures of these huge social movements in the ‘60s and ‘70s. There had been this glimpse of collective power that a lot of people really thought was going to change the world, and suddenly they’re living through Thatcherism and Reaganism. And there is this turn towards the self, towards the body as the site of control.

Kneeling before the temple of the body also has fascist roots. Historically, certain ideals of human fitness were a way to communicate the value of citizens.Whenever you are working within a system of a hierarchy of humans and bodies, then you’re in fascism territory. I think that it made perfect sense that Nazis were body obsessives who fetishized the natural and the hyperfit form and genes.

There is a connection between certain kinds of new age ideas and health fads and the fascist project

After the second world war, a lot of people in the world of wellness ran in the opposite direction. But there are some ways in which they are natural affinities and they’re finding each other again

there is a way the quest for wellness and hyper fitness becomes obsessive.

But the spread of misinformation across wellness culture was likely attributable to more complex factors, including the limits of conventional medicine and the areas of health that are understudied or dismissed.

Ehrenreich is trying to understand why this exploded in the 1980s. The whole aerobics craze, the whole jogging craze. You know, how does somebody like Jerry Rubin, a member of the Yippies, turn into a health evangelist in the 1980s?

in lots of ways this is what Naomi Wolf was trying to understand in the Beauty Myth. Why was there so much more of a focus in the 1980s on personal appearance? She makes the case that beauty became a third shift for women: there was the work shift, there was the home shift, and on top of that, women were now also expected to look like professional beauties.

Barbara Ehrenreich wrote about this really beautifully in her book about wellness culture, where she talks about the silence of the gyms. This is a collective space, right? Why aren’t people chitchatting? But often gyms are very silent and she speculates that maybe it’s because people are talking to someone, it’s just not the other people in the gym, it’s somebody in their head. They’re trying to tame their body into being another kind of body, a perfected body.

Then you have all of these entrepreneurial wellness figures who come in and say, individuals must take charge of their own bodies as their primary sites of influence, control and competitive edge.

the flip side of the idea that your competitive edge is your body is that the people who don’t have bodies as fit or strong as yours somehow did something wrong or are less deserving of access, less deserving even of life.

And that is unfortunately all too compatible with far-right notions of natural hierarchies, genetic superiority and disposable people.

We should be compassionate with ourselves in terms of why we look away. There are lots of ways of distracting oneself from unbearable realities. Conspiracy theories are a kind of distraction. So is hyper fitness, this turn towards the self.

The compassion comes in where we acknowledge that there’s a reason why it is so hard to look at the reality of what has been unveiled by these overlapping crises – you could call it a polycrisis: of the pandemic, climate change, massive racial and economic inequality, realizing that your country was founded on a lie, that the national narratives that you grew up on left out huge parts in the story.

All of this is hard to bear.

Because we live in a hyper-individualist culture, we try to bear it on our own and we should not be surprised that we’re cracking under the weight of that, because we can’t bear it alone.

the weight of our historical moment. We really are alive on the knife’s edge of whether or not this earth is going to be habitable for our species. That is not something that we can handle just on our own.

So we need to reach towards each other. That’s really tricky work. It’s a lot easier to come together and agree on things that are not working and things that are bad than it is to come together and develop a horizon of how things could be better.

Things could be beautiful, things could be livable. There could be a world where everyone belongs. But I don’t think we can bear the reality of our moment unless we can imagine something else.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NIMROD NOVIK: The group that worked on it, called Commanders for Israel’s Security, it’s over 500 Israeli retired generals, as well as their equivalents from the Mossad, Shin Bet Security, National Security Council, the entire Israeli security establishment. And we formed a team. We felt that Israeli policy was far too reactive and far too conservative for the good of the country, national security, short and long-term.

We had not anticipated the trauma of Oct. 7, but we certainly anticipated things getting from bad to worse, unless Israel changes course.

we came up with a plan that suggested even though a two-state solution, as you said, is not on this side of the horizon, but given that eventually, it’s the only solution that we believe serves Israel’s security and well-being long-term, as a strong Jewish democracy, we mapped out what can and should be done in the coming two, three years to reverse the slide towards the disaster of a one-state solution.

NIMROD NOVIK: There were primarily two governing concepts, if you will, of the Israeli policy. Again, calling it policy is giving it more credit than deserved. Israeli government have been reluctant to determine the end game of our relationship with the Palestinians. Where do we want to see ourselves and them two years, five years, 50 years from now? No decision has been made since the Oslo era.

As a result, what we’ve seen was a policy based on insisting on separating the Gaza Strip, ruled by Hamas from the West Bank, ruled sort of by the Palestinian Authority. Separation was one principle

And the other one was dubbed status quo, even though it was an illusion, because nothing was static about it. As a matter of fact, creeping annexation has been accelerating under various governments.

The more territory was taken by settlements, the more extreme settlers were conducting violent raids into Palestinian civil populations. The more the Palestinian Authority, internally defective, becoming more and more authoritarian, more and more detached from its own constituents, less responsive, less capable of governance, losing control over large swaths of West Bank territory, forcing the I.D.F. to enter more and more

It was a slide into a state where the Palestinian Authority would cease to function as the promise of the nucleus of a Palestinian state.

If we look at it today, it’s already perhaps the municipal government of the city of Ramallah, rather than of the West Bank, and weakening the Palestinian Authority by choking it financially. By not allowing it to demonstrate to its people that it is the vehicle that will bring them one day to their aspiration of statehood, on the one hand, and making sure that Hamas controls Gaza, the two tracks spelled disaster.

So I must confess, we had not anticipated that the disaster will look the way it did on Oct. 7, but we certainly realized that the policy in Gaza of rounds of violence every year, every two years, every 18 months, and buying off relative tranquility by funding Hamas through the auspices of Qatar, allowing it to arm and rearm, the inherent contradictions in the policy were quite apparent

There’s a right-wing one-state solution. I think when you mentioned the finance minister, Bezalel Smotrich, I think if you read things he has written in the past, he is looking for a one-state solution. He wants to crush Palestinian dreams of statehood and repress Palestinians sufficiently that they stop believing they can ever have anything better and eventually content themselves to Israeli rule and live quietly within that in order to gain better lives.

NIMROD NOVIK: I’ll put it bluntly. I believe that a two-state solution is inevitable, not because we wish it and not because it’s nice, not because Palestinians deserve self-determination — which they do, but that’s not a historic imperative. I believe that the two-state solution is inevitable because these two people are not going to live happily ever after under one roof.

For that to happen, for the two people to stay in one state, one of two things have to happen. Either Israelis will agree to grant Palestinian equal rights in that one state and therefore become a minority, or at least, a slim majority in our own country, and that’s never going to happen. Israelis are not going to agree to be less than the overwhelming majority in our own country.

Or Palestinians will agree forever to forgo equal rights, which I suspect is as unreasonable expectation as the other. So we will separate.

NIMROD NOVIK: Civil separation with overall security control — continued security control until a two-state agreement ushers in alternative security arrangements, is a concept that basically suggests reversing the creeping annexation, which is no longer creeping. It’s now galloping.

So the idea is to start reversing the slide towards one-state reality in the opposite direction, of reducing the friction between the two populations, increasing the capacity of the P.A. to perform, while maintaining the overall security controlled by Israel until a deal is struck.

You often hear when you talk to people in Israel about different paths that could be taken. Well, we don’t have anybody to negotiate with. The Palestinian Authority doesn’t have credibility. Hamas wants our destruction

And a core premise of the report is that there are things Israel can do unilaterally, that it doesn’t need a partner to do things that will make the situation better from its perspective and create conditions maybe for deals in the future. So tell me what is in Israel’s power here. What would you actually recommend to do tangibly?

NIMROD NOVIK: It’s not a genetic deformation of the Palestinians that they cannot govern themselves. This is nonsense. We had a period after the second intifada, the years 2007, 2008, where the Palestinian Authority, there was a prime minister by the name of Salam Fayyad. First, he was finance minister, later on prime minister, who revitalized the Palestinian Authority in a dramatic way. The authority was on the rise. People were proud in it, its own population. They could have won elections at that point.

And then Netanyahu was elected in 2009. Now, obviously, we are the strongest party. We hold most of the cards by far. And when we decide that we are going to choke the Palestinian Authority, the Palestinian Authority will choke

Now the second trend that happened was that Mahmoud Abbas, President Abbas, known as Abu Mazen, the early Abu Mazen was a very different person than the late one with whom we are dealing today. He became increasingly nondemocratic, authoritarian, autocratic, paranoid, removing from his vicinity and from position of power all the best and brightest that were working during that era

. Things went from bad to worse, Israel doing its share in weakening the P.A. and the P.A. leadership became more claustrophobic. All these can change.

At the moment, the West Bank is a Swiss cheese. It’s 169 islands of Palestinian-controlled areas surrounded each by Israeli-controlled territory. So we wanted to reduce that by half so that contiguity will have a security, law and order, and economic well-being effect.

We suggested a host of economic measures that enable the Palestinian Authority to deliver for the people, which is the opposite of what’s happening now, when our minister of finance is choking the Palestinian Authority by withholding funds that are theirs by the agreement Israel collects taxes for the Palestinian Authority, VAT and others. And we are supposed to automatically transfer them to the Palestinian Authority. It’s the main chunk of their budget.

who got the future most right? For the truth is that dystopia is now, not in some future date.

Science fiction provides us with a large sample of imagined discontinuities that might not occur if we only looked backwards.

Fahrenheit 451 (published in 1953 but set in 1999) describes an illiberal America where books are banned and the job of firemen is to burn them. (Though the novel is sometimes interpreted as a critique of McCarthyism, Bradbury’s real message was that the preference of ordinary people for the vacuous entertainment of TV and the willingness of religious minorities to demand censorship together posed a creeping threat to the book as a form for serious content.)

In a remarkable letter written in October 1949, Aldous Huxley — who had been Orwell’s French teacher at Eton — warned him that he was capturing his own present rather than the likely future. ‘The philosophy of the ruling minority in Nineteen Eighty-Four,’ Huxley wrote, ‘is a sadism which has been carried to its logical conclusion… Whether in actual fact the policy of the boot-on-the-face can go on indefinitely seems doubtful. My own belief is that the ruling oligarchy will find less arduous and wasteful ways of governing and of satisfying its lust for power, and these ways will resemble those which I described in Brave New World’. Huxley’s Brave New World (1932) is a very different dystopia. Citizens submit to a caste system, conditioned to be content with physical pleasure. Self-medication (‘soma’), constant entertainment (the ‘feelies’), regular holidays and ubiquitous sexual titillation are the basis for mass compliance. Censorship and propaganda play a part, but overt coercion is rarely visible. The West today seems more Huxley than Orwell: a world more of corporate distraction than state brutality.

Yet none of these authors truly foresaw our networked world, which has combined the rising technological acceleration with a slackening of progress in other areas, such as nuclear energy, and a degeneration of governance. The real prophets are less known figures, like John Brunner, whose Stand on Zanzibar (1968) is set at a time — 2010 — when population pressure has caused social division and political extremism. Despite the threat of terrorism, US corporations are booming, thanks to a supercomputer. China is America’s new rival. Europe has united. Brunner envisaged affirmative action, genetic engineering, Viagra, Detroit’s collapse, satellite TV, in-flight video, gay marriage, laser printing, electric cars, the decriminalisation of marijuana and the decline of tobacco. There’s even a progressive president (albeit of the Africa state of Beninia, not America) named ‘Obomi’

With comparable prescience, William Gibson’s Neuromancer (1984) anticipates the world wide web and AI. Opening in the dystopian Japanese underworld of Chiba City, it imagines a global computer network in cyberspace called the ‘matrix’. Neal Stephenson’s Snow Crash (1992), which was especially popular among Facebook employees in the company’s early years, foresaw corporate overreach and virtual reality in an almost anarchic America. The state has withered away in California; everything has been privatised. Most people spend half their time in virtual reality, where their avatars have more fun than they themselves do in the real world. Meanwhile, flotillas of refugees approach via the Pacific. These cyberpunk Americas are much closer to the US in 2021 than the fascist dystopias of Lewis, Atwood or Roth.

Orwell and Huxley — have been outflanked when it comes to making sense of today’s totalitarian countries

Take China, which better resembles Yevgeny Zamyatin’s We: a book written in 1921, but suppressed by the Bolsheviks. It is set in a future ‘One State’ led by ‘the Benefactor’, where the ‘ciphers’ — who have numbers, not names, and wear standardised ‘unifs’ — are under constant surveillance. All apartments are made of glass, with curtains that can be drawn only when one is having state-licensed sex. Faced with insurrection, the omnipotent Benefactor orders the mass lobotomisation of ciphers, as the only way to preserve universal happiness is to abolish the imagination.

Chan Koonchung’s The Fat Years (2009) — which is banned in China. In this story, tap water is laced with drugs that render people docile, but at a cost. The month of February 2011 has been removed from public records and popular memory. This was when drastic emergency measures were introduced to stabilise the Chinese economy and assert China’s primacy in east Asia. Chan is one of a number of recent Chinese authors who have envisioned the decline of America, the corollary of China’s rise. The Fat Years is set in an imagined 2013, after a second western financial crisis makes China the world’s no. 1 economy.

Liu Cixin’s The Three-Body Problem (2006), a Chinese nanotechnology expert and a Beijing cop lead the global defence against an alien invasion that’s the fault of a misanthropic Chinese physicist.

that would be on par with what we experience with flu viruses, which scientists estimate hit us about every two to five years, less often in adulthood. It also matches up well with the documented cadence of the four other coronaviruses that seasonally trouble humans, and cause common colds.

For now, every infection, and every subsequent reinfection, remains a toss of the dice. “Really, it’s a gamble,” says Ziyad Al-Aly, a clinical epidemiologist and long-COVID researcher at Washington University in St. Louis. Vaccination and infection-induced immunity may load the dice against landing on severe disease, but that danger will never go away completely, and scientists don’t yet know what happens to people who contract “mild” COVID over and over again

Or maybe not. This virus seems capable of tangling into just about every tissue in the body, affecting organs such as the heart, brain, liver, kidneys, and gut; it has already claimed the lives of millions, while saddling countless others with symptoms that can linger for months or years.

considering our current baseline, “less dangerous” could still be terrible—and it’s not clear exactly where we’re headed. When it comes to reinfection, we “just don’t know enough,”

Perhaps, as several experts have posited since the pandemic’s early days, SARS-CoV-2 will just become the fifth cold-causing coronavirus.

Bodies, wised up to the virus’s quirks, can now react more quickly, clobbering it with sharper and speedier strikes.

Future versions of SARS-CoV-2 could continue to shape-shift out of existing antibodies’ reach, as coronaviruses often do. But the body is flush with other fighters that are much tougher to bamboozle—among them, B cells and T cells that can quash a growing infection before it spirals out of control

Those protections tend to build iteratively, as people see pathogens or vaccines more often. People vaccinated three times over, for instance, seem especially well equipped to duke it out with all sorts of SARS-CoV-2 variants, including Omicron and its offshoots.

promising patterns: Second infections and post-vaccination infections “are significantly less severe,” she told me, sometimes to the point where people don’t notice them at all

A third or fourth bout might be more muted still; the burden of individual diseases may be headed toward an asymptote of mildness that holds for many years

Gordon and Swartz are both hopeful that the slow accumulation of immunity will also slash people’s chances of developing long COVID.

Immunity, though, is neither binary nor permanent. Even if SARS-CoV-2’s assaults are blunted over time, there are no guarantees about the degree to which that happens, or how long it lasts.

A slew of factors could end up weighting the dice toward severe disease—among them, a person’s genetics, age, underlying medical conditions, health-care access, and frequency or magnitude of exposure to the virus.

for everyone else, no amount of viral dampening can totally eliminate the chance, however small it may be, of getting very sick.

Long COVID, too, might remain a possibility with every discrete bout of illness. Or maybe the effects of a slow-but-steady trickle of minor, fast-resolving infections would sum together, and bring about the condition.

Every time the body’s defenses are engaged, it “takes a lot of energy, and causes tissue damage,” Thomas told me. Should that become a near-constant barrage, “that’s probably not great for you.”

Bodies are resilient, especially when they’re offered time to rest, and she doubts that reinfection with a typically ephemeral virus such as SARS-CoV-2 would cause mounting damage. “The cumulative effect is more likely to be protective than detrimental,” she said, because of the immunity that’s laid down each time.

people who have caught the virus twice or thrice may be more likely to become long-haulers than those who have had it just once.

Some other microbes, when they reinvade us, can fire up the immune system in unhelpful ways, driving bad bouts of inflammation that burn through the body, or duping certain defensive molecules into aiding, rather than blocking, the virus’s siege. Researchers don’t think SARS-CoV-2 will do the same. But this pathogen is “much more formidable than even someone working on coronaviruses would have expected,

Seasonal encounters with pathogens other than SARS-CoV-2 don’t often worry us—but perhaps that’s because we’re still working to understand their toll. “Have we been underestimating long-term consequences from other repeat infections?” Thomas said. “The answer is probably, almost certainly, yes.”

the rhythm of reinfection isn’t just about the durability of immunity or the pace of viral evolution. It’s also about our actions and policies, and whether they allow the pathogen to transmit and evolve. Strategies to avoid infection—to make it as infrequent as possible, for as many people as possible—remain options, in the form of vaccination, masking, ventilation, paid sick leave, and more.

“There are still very good reasons” to keep exposures few and far between, Landon, of the University of Chicago, told me. Putting off reinfection creates fewer opportunities for harm: The dice are less likely to land on severe disease (or chronic illness) when they’re rolled less often overall. It also buys us time to enhance our understanding of the virus, and improve our tools to fight it.

The outlooks of the experts I spoke with spanned the range from optimism to pessimism, though all agreed that uncertainty loomed. Until we know more, none were keen to gamble with the virus—or with their own health. Any reinfection will likely still pose a threat, “even if it’s not the worst-case scenario,” Abdool Karim told me. “I wouldn’t want to put myself in that position.”

The U.S. logs fewer than 1,000 annual deaths out of millions of documented cases

this is more a nauseating nuisance than a public-health crisis. In most people, norovirus triggers, at most, a few miserable days of GI distress that can include vomiting, diarrhea, and fevers, then resolves on its own; the keys are to stay hydrated and avoid spreading it to anyone vulnerabl

norovirus is the most common cause of foodborne illness in the United States.)

the virus is far more deadly in parts of the world with limited access to sanitation and potable water.

Still, fighting norovirus isn’t easy, as plenty of parents can attest. The pathogen, which prompts the body to expel infectious material from both ends of the digestive tract, is seriously gross and frustratingly hardy. Even the old COVID standby, a spritz of hand sanitizer, doesn’t work against it—the virus is encased in a tough protein shell that makes it insensitive to alcohol.

At an extreme, a single gram of feces—roughly the heft of a jelly bean—could contain as many as 5.5 billion infectious doses, enough to send the entire population of Eurasia sprinting for the toilet.

norovirus mainly targets the gut, and spreads especially well when people swallow viral particles that have been released in someone else’s vomit or stool.

direct contact with those substances, or the food or water they contaminate, may not even be necessary: Sometimes people vomit with such force that the virus gets aerosolized; toilets, especially lidless ones, can send out plumes of infection

If the spittle finding holds for humans, then talking, singing, and laughing in close proximity could be risky too.

Once emitted into the environment, norovirus particles can persist on surfaces for days—making frequent hand-washing and surface disinfection key measures to prevent spread

Handshakes and shared meals tend to get dicey during outbreaks, along with frequently touched items such as utensils, door handles, and phones.

One 2012 study pointed to a woven plastic grocery bag as the source of a small outbreak among a group of teenage soccer players; the bag had just been sitting in a bathroom used by one of the girls when she fell sick the night before.

Once a norovirus transmission chain begins, it can be very difficult to break. The virus can spread before symptoms start, and then for more than a week after they resolve

Once the virus arrives, the entire family is almost sure to be infected. Baldridge, who has two young children, told me that her household has weathered at least four bouts of norovirus in the past several years.

Roughly 20 percent of European populations, for instance, are genetically resistant to common norovirus strains. “So you can hope,” Frenck told me. For the rest of us, it comes down to hygiene

Altan-Bonnet recommends diligent hand-washing, plus masking to ward off droplet-borne virus. Sick people should isolate themselves if they can. “And keep your saliva to yourself,” she told me.

The family fastidiously scrubbed their hands with hot water and soap, donned disposable gloves when touching shared surfaces, and took advantage of the virus’s susceptibility to harsh chemicals and heat. When her son threw up on the floor, Cameron sprayed it down with bleach; when he vomited on his quilt, she blasted it twice in the washing machine on the sanitizing setting, then put it through the dryer at a super high temp

After three years of COVID, the world has gotten used to thinking about infections in terms of airways. “We need to recalibrate,” Bhumbra told me, “and remember that other things exist.”

While sex is a category that refers broadly to physiology, a person's gender is an innate sense of identity. The factors that go into determining the sex listed on a birth certificate may include anatomy, genetics and hormones, and there is broad natural variation in each of these categories. For this reason, critics have said the language of "biological sex," as used in DeSantis' proclamation, is overly simplistic and misleading.A 2017 report in the journal Sports Medicine that reviewed several related studies found "no direct or consistent research" on trans people having an athletic advantage over their cisgender peers, at any state of their transition, and critics say postures like DeSantis' will only add to the discrimination that trans people face, particularly trans youth.

So far this year, Iowa and South Dakota have approved legislation banning transgender women and girls from participating on sports teams consistent with their gender at accredited schools and colleges. And last year, Alabama, Arkansas, Florida, Mississippi, Montana, Tennessee, Texas and West Virginia enacted similar sports bans, infuriating LGBTQ advocates, who argue conservatives are creating an issue where there isn't one.

Recent experiments suggest that XBB paid a steep price for its power to evade immunity. The mutations allow it to escape antibodies by altering the shape of the protein, called spike, that covers its surface. But some of those mutations also make it harder for XBB spike proteins to grab tightly to cells — the first step required for an infection.

That loose grip may have lessened XBB’s advantage against other forms of the virus. In late 2022, it jostled alongside a number of other Omicron subvariants. In Singapore, XBB caused a surge in October, for example, while remaining rare in many other parts of the world.

As XBB multiplied, it continued to mutate into new forms. The earliest samples of XBB.1.5 were isolated in October in New York. The new subvariant gained one crucial mutation, known as F486P.

Yunlong Cao of Peking University and his colleagues tested out XBB.1.5 in dishes of cells, comparing how it fared against earlier forms of XBB. The researchers found that the F486P mutation allowed XBB.1.5 to grab tightly to cells again. But the new subvariant could still evade antibodies as well as earlier forms of XBB.

In Connecticut, for example, Nathan Grubaugh at Yale University and his colleagues found that by mid-December, other Omicron subvariants were falling. Only XBB.1.5 cases were growing. Dr. Grubaugh estimates that it is about 20 percent more transmissible than BQ.1, which had been the dominant form.

How severe XBB.1.5 infections are compared with other forms of the coronavirus is not yet clear. “It’s serious,” Dr. Grubaugh said. “I just don’t necessarily know if it’s really more serious than some of the other Omicron lineages in terms of the overall impact.”

XBB.1.5 has already spread to other countries, and is growing rapidly in Germany, Denmark and elsewhere in Europe

Scientists are already scanning new sequences being uploaded to an international database called GISAID in the hopes of spotting an upgraded version of XBB.1.5. But their job is getting harder because governments are pulling back on sequencing efforts. “Worldwide, sequencing has taken a real hit,” Dr. Peacock said.

The United States, which once lagged behind other nations, has managed to maintain a fairly strong sequencing effort. Without it, Dr. Peacock said, XBB.1.5 might have stayed below the radar for much longer. If XBB.1.5’s next generation is evolving somewhere with little sequencing, it may go undetected for some time to come.

Dr. Lemieux said that paring back on sequencing was a mistake, given how many infections and deaths the virus is still causing. “This is a part of public health,” he said.

The ordinary fixes—the kind that draw on people’s will, and require eating less and moving more—rarely have a large or lasting effect. Indeed, America itself has suffered through a long, maddening history of failed attempts to change its habits on a national scale: a yo-yo diet of well-intentioned treatments, policies, and other social interventions that only ever lead us back to where we started

Through it all, obesity rates keep going up; the diabetes epidemic keeps worsening.

The most recent miracle, for Barb as well as for the nation, has come in the form of injectable drugs. In early 2021, the Danish pharmaceutical company Novo Nordisk published a clinical trial showing remarkable results for semaglutide, now sold under the trade names Wegovy and Ozempic.

Patients in the study who’d had injections of the drug lost, on average, close to 15 percent of their body weight—more than had ever been achieved with any other drug in a study of that size. Wadden knew immediately that this would be “an incredible revolution in the treatment of obesity.”

Many more drugs are now racing through development: survodutide, pemvidutide, retatrutide. (Among specialists, that last one has produced the most excitement: An early trial found an average weight loss of 24 percent in one group of participants.

In the United States, an estimated 189 million adults are classified as having obesity or being overweight

The drugs don’t work for everyone. Their major side effects—nausea, vomiting, and diarrhea—can be too intense for many patients. Others don’t end up losing any weight

For the time being, just 25 percent of private insurers offer the relevant coverage, and the cost of treatment—about $1,000 a month—has been prohibitive for many Americans.

The drugs have already been approved not just for people with diabetes or obesity, but for anyone who has a BMI of more than 27 and an associated health condition, such as high blood pressure or cholesterol. By those criteria, more than 140 million American adults already qualify

if this story goes the way it’s gone for other “risk factor” drugs such as statins and antihypertensives, then the threshold for prescriptions will be lowered over time, inching further toward the weight range we now describe as “normal.”

How you view that prospect will depend on your attitudes about obesity, and your tolerance for risk

The first GLP-1 drug to receive FDA approval, exenatide, has been used as a diabetes treatment for more than 20 years. No long-term harms have been identified—but then again, that drug’s long-term effects have been studied carefully only across a span of seven years

the data so far look very good. “These are now being used, literally, in hundreds of thousands of people across the world,” she told me, and although some studies have suggested that GLP-1 drugs may cause inflammation of the pancreas, or even tumor growth, these concerns have not borne out.

adolescents are injecting newer versions of these drugs, and may continue to do so every week for 50 years or more. What might happen over all that time?

“All of us, in the back of our minds, always wonder, Will something show up?  ” Although no serious problems have yet emerged, she said, “you wonder, and you worry.”

in light of what we’ve been through, it’s hard to see what other choices still remain. For 40 years, we’ve tried to curb the spread of obesity and its related ailments, and for 40 years, we’ve failed. We don’t know how to fix the problem. We don’t even understand what’s really causing it. Now, again, we have a new approach. This time around, the fix had better work.

The fen-phen revolution arrived at a crucial turning point for Wadden’s field, and indeed for his career. By then he’d spent almost 15 years at the leading edge of research into dietary interventions, seeing how much weight a person might lose through careful cutting of their calories.

But that sort of diet science—and the diet culture that it helped support—had lately come into a state of ruin. Americans were fatter than they’d ever been, and they were giving up on losing weight. According to one industry group, the total number of dieters in the country declined by more than 25 percent from 1986 to 1991.

Rejecting diet culture became something of a feminist cause. “A growing number of women are joining in an anti-diet movement,” The New York Times reported in 1992. “They are forming support groups and ceasing to diet with a resolve similar to that of secretaries who 20 years ago stopped getting coffee for their bosses.

Now Wadden and other obesity researchers were reaching a consensus that behavioral interventions might produce in the very best scenario an average lasting weight loss of just 5 to 10 percent

National surveys completed in 1994 showed that the adult obesity rate had surged by more than half since 1980, while the proportion of children classified as overweight had doubled. The need for weight control in America had never seemed so great, even as the chances of achieving it were never perceived to be so small.

Wadden wasn’t terribly concerned, because no one in his study had reported any heart symptoms. But ultrasounds revealed that nearly one-third of them had some degree of leakage in their heart valves. His “cure for obesity” was in fact a source of harm.

In December 1994, the Times ran an editorial on what was understood to be a pivotal discovery: A genetic basis for obesity had finally been found. Researchers at Rockefeller University were investigating a molecule, later named leptin, that gets secreted from fat cells and travels to the brain, and that causes feelings of satiety. Lab mice with mutations in the leptin gene—importantly, a gene also found in humans—overeat until they’re three times the size of other mice. “The finding holds out the dazzling hope,”

In April 1996, the doctors recommended yes: Dexfenfluramine was approved—and became an instant blockbuster. Patients received prescriptions by the hundreds of thousands every month. Sketchy wellness clinics—call toll-free, 1-888-4FEN-FEN—helped meet demand. Then, as now, experts voiced concerns about access. Then, as now, they worried that people who didn’t really need the drugs were lining up to take them. By the end of the year, sales of “fen” alone had surpassed $300 million.

It was nothing less than an awakening, for doctors and their patients alike. Now a patient could be treated for excess weight in the same way they might be treated for diabetes or hypertension—with a drug they’d have to take for the rest of their life.

the article heralded a “new understanding of obesity as a chronic disease rather than a failure of willpower.”

News had just come out that, at the Mayo Clinic in Minnesota, two dozen women taking fen-phen—including six who were, like Barb, in their 30s—had developed cardiac conditions. A few had needed surgery, and on the operating table, doctors discovered that their heart valves were covered with a waxy plaque.

Americans had been prescribed regular fenfluramine since 1973, and the newer drug, dexfenfluramine, had been available in France since 1985. Experts took comfort in this history. Using language that is familiar from today’s assurances regarding semaglutide and other GLP-1 drugs, they pointed out that millions were already on the medication. “It is highly unlikely that there is anything significant in toxicity to the drug that hasn’t been picked up with this kind of experience,” an FDA official named James Bilstad would later say in a Time cover story headlined “The Hot New Diet Pill.

“I know I can’t get any more,” she told Williams. “I have to use up what I have. And then I don’t know what I’m going to do after that. That’s the problem—and that is what scares me to death.” Telling people to lose weight the “natural way,” she told another guest, who was suggesting that people with obesity need only go on low-carb diets, is like “asking a person with a thyroid condition to just stop their medication.”

She’d gone off the fen-phen and had rapidly regained weight. “The voices returned and came back in a furor I’d never heard before,” Barb later wrote on her blog. “It was as if they were so angry at being silenced for so long, they were going to tell me 19 months’ worth of what they wanted me to hear. I was forced to listen. And I ate. And I ate. And ate.”

For Barb, rapid weight loss has brought on a different metaphysical confusion. When she looks in the mirror, she sometimes sees her shape as it was two years ago. In certain corners of the internet, this is known as “phantom fat syndrome,” but Barb dislikes that term. She thinks it should be called “body integration syndrome,” stemming from a disconnect between your “larger-body memory” and “smaller-body reality.

In 2003, the U.S. surgeon general declared obesity “the terror within, a threat that is every bit as real to America as the weapons of mass destruction”; a few months later, Eric Finkelstein, an economist who studies the social costs of obesity, put out an influential paper finding that excess weight was associated with up to $79 billion in health-care spending in 1998, of which roughly half was paid by Medicare and Medicaid. (Later he’d conclude that the number had nearly doubled in a decade.

In 2004, Finkelstein attended an Action on Obesity summit hosted by the Mayo Clinic, at which numerous social interventions were proposed, including calorie labeling in workplace cafeterias and mandatory gym class for children of all grades.

he message at their core, that soda was a form of poison like tobacco, spread. In San Francisco and New York, public-service campaigns showed images of soda bottles pouring out a stream of glistening, blood-streaked fat. Michelle Obama led an effort to depict water—plain old water—as something “cool” to drink.

Soon, the federal government took up many of the ideas that Brownell had helped popularize. Barack Obama had promised while campaigning for president that if America’s obesity trends could be reversed, the Medicare system alone would save “a trillion dollars.” By fighting fat, he implied, his ambitious plan for health-care reform would pay for itself. Once he was in office, his administration pulled every policy lever it could.

Michelle Obama helped guide these efforts, working with marketing experts to develop ways of nudging kids toward better diets and pledging to eliminate “food deserts,” or neighborhoods that lacked convenient access to healthy, affordable food. She was relentless in her public messaging; she planted an organic garden at the White House and promoted her signature “Let’s Move!” campaign around the country.

An all-out war on soda would come to stand in for these broad efforts. Nutrition studies found that half of all Americans were drinking sugar-sweetened beverages every day, and that consumption of these accounted for one-third of the added sugar in adults’ diets. Studies turned up links between people’s soft-drink consumption and their risks for type 2 diabetes and obesity. A new strand of research hinted that “liquid calories” in particular were dangerous to health.

when their field lost faith in low-calorie diets as a source of lasting weight loss, the two friends went in opposite directions. Wadden looked for ways to fix a person’s chemistry, so he turned to pharmaceuticals. Brownell had come to see obesity as a product of our toxic food environment: He meant to fix the world to which a person’s chemistry responded, so he started getting into policy.

The social engineering worked. Slowly but surely, Americans’ lamented lifestyle began to shift. From 2001 to 2018, added-sugar intake dropped by about one-fifth among children, teens, and young adults. From the late 1970s through the early 2000s, the obesity rate among American children had roughly tripled; then, suddenly, it flattened out.

although the obesity rate among adults was still increasing, its climb seemed slower than before. Americans’ long-standing tendency to eat ever-bigger portions also seemed to be abating.

sugary drinks—liquid candy, pretty much—were always going to be a soft target for the nanny state. Fixing the food environment in deeper ways proved much harder. “The tobacco playbook pretty much only works for soda, because that’s the closest analogy we have as a food item,

that tobacco playbook doesn’t work to increase consumption of fruits and vegetables, he said. It doesn’t work to increase consumption of beans. It doesn’t work to make people eat more nuts or seeds or extra-virgin olive oil.

Careful research in the past decade has shown that many of the Obama-era social fixes did little to alter behavior or improve our health. Putting calorie labels on menus seemed to prompt at most a small decline in the amount of food people ate. Employer-based wellness programs (which are still offered by 80 percent of large companies) were shown to have zero tangible effects. Health-care spending, in general, kept going up.

From the mid-1990s to the mid-2000s, the proportion of adults who said they’d experienced discrimination on account of their height or weight increased by two-thirds, going up to 12 percent. Puhl and others started citing evidence that this form of discrimination wasn’t merely a source of psychic harm, but also of obesity itself. Studies found that the experience of weight discrimination is associated with overeating, and with the risk of weight gain over time.

obesity rates resumed their ascent. Today, 20 percent of American children have obesity. For all the policy nudges and the sensible revisions to nutrition standards, food companies remain as unfettered as they were in the 1990s, Kelly Brownell told me. “Is there anything the industry can’t do now that it was doing then?” he asked. “The answer really is no. And so we have a very predictable set of outcomes.”

she started to rebound. The openings into her gastric pouch—the section of her stomach that wasn’t bypassed—stretched back to something like their former size. And Barb found ways to “eat around” the surgery, as doctors say, by taking food throughout the day in smaller portions

Bariatric surgeries can be highly effective for some people and nearly useless for others. Long-term studies have found that 30 percent of those who receive the same procedure Barb did regain at least one-quarter of what they lost within two years of reaching their weight nadir; more than half regain that much within five years.

if the effects of Barb’s surgery were quickly wearing off, its side effects were not: She now had iron, calcium, and B12 deficiencies resulting from the changes to her gut. She looked into getting a revision of the surgery—a redo, more or less—but insurance wouldn’t cover it

She found that every health concern she brought to doctors might be taken as a referendum, in some way, on her body size. “If I stubbed my toe or whatever, they’d just say ‘Lose weight.’ ” She began to notice all the times she’d be in a waiting room and find that every chair had arms. She realized that if she was having a surgical procedure, she’d need to buy herself a plus-size gown—or else submit to being covered with a bedsheet when the nurses realized that nothing else would fit.

Barb grew angrier and more direct about her needs—You’ll have to find me a different chair, she started saying to receptionists. Many others shared her rage. Activists had long decried the cruel treatment of people with obesity: The National Association to Advance Fat Acceptance had existed, for example, in one form or another, since 1969; the Council on Size & Weight Discrimination had been incorporated in 1991. But in the early 2000s, the ideas behind this movement began to wend their way deeper into academia, and they soon gained some purchase with the public.

“Our public-health efforts to address obesity have failed,” Eric Finkelstein, the economist, told me.

Others attacked the very premise of a “healthy weight”: People do not have any fundamental need, they argued, morally or medically, to strive for smaller bodies as an end in itself. They called for resistance to the ideology of anti-fatness, with its profit-making arms in health care and consumer goods. The Association for Size Diversity and Health formed in 2003; a year later, dozens of scholars working on weight-related topics joined together to create the academic field of fat studies.

As the size-diversity movement grew, its values were taken up—or co-opted—by Big Business. Dove had recently launched its “Campaign for Real Beauty,” which included plus-size women. (Ad Age later named it the best ad campaign of the 21st century.) People started talking about “fat shaming” as something to avoid

By 2001, Bacon, who uses they/them pronouns, had received their Ph.D. and finished a rough draft of a book, Health at Every Size, which drew inspiration from a broader movement by that name among health-care practitioners

But something shifted in the ensuing years. In 2007, Bacon got a different response, and the book was published. Health at Every Size became a point of entry for a generation of young activists and, for a time, helped shape Americans’ understanding of obesity.

Some experts were rethinking their advice on food and diet. At UC Davis, a physiologist named Lindo Bacon who had struggled to overcome an eating disorder had been studying the effects of “intuitive eating,” which aims to promote healthy, sustainable behavior without fixating on what you weigh or how you look

The heightened sensitivity started showing up in survey data, too. In 2010, fewer than half of U.S. adults expressed support for giving people with obesity the same legal protections from discrimination offered to people with disabilities. In 2015, that rate had risen to three-quarters.

In Bacon’s view, the 2000s and 2010s were glory years. “People came together and they realized that they’re not alone, and they can start to be critical of the ideas that they’ve been taught,” Bacon told me. “We were on this marvelous path of gaining more credibility for the whole Health at Every Size movement, and more awareness.”

that sense of unity proved short-lived; the movement soon began to splinter. Black women have the highest rates of obesity, and disproportionately high rates of associated health conditions. Yet according to Fatima Cody Stanford, an obesity-medicine physician at Harvard Medical School, Black patients with obesity get lower-quality care than white patients with obesity.

That system was exactly what Bacon and the Health at Every Size movement had set out to reform. The problem, as they saw it, was not so much that Black people lacked access to obesity medicine, but that, as Bacon and the Black sociologist Sabrina Strings argued in a 2020 article, Black women have been “specifically targeted” for weight loss, which Bacon and Strings saw as a form of racism

But members of the fat-acceptance movement pointed out that their own most visible leaders, including Bacon, were overwhelmingly white. “White female dietitians have helped steal and monetize the body positive movement,” Marquisele Mercedes, a Black activist and public-health Ph.D. student, wrote in September 2020. “And I’m sick of it.”

Tensions over who had the standing to speak, and on which topics, boiled over. In 2022, following allegations that Bacon had been exploitative and condescending toward Black colleagues, the Association for Size Diversity and Health expelled them from its ranks and barred them from attending its events.