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The national unemployment rate, as calculated by the National Bureau of Statistics, accounts only for urban unemployment, which stands at just above 5 percent and is believed to be underestimated. The average monthly income of migrant workers was $630 in 2022, or less than half the income of those working for the government. And that data is flawed because it includes only months when a worker has a job.

Mr. Xi said in his speech that the mass return of migrant workers in 2008 and 2020 had not caused any social problems because they “have land and houses back in their home villages so they can return to cultivate the land, have food to eat, and work on something.”

But the prospect of moving back to the villages is often bleak and even scary, especially for younger migrant workers who have spent their adult lives in the cities. They can see what awaits them. Their parents and grandparents may need to work until they physically cannot anymore and hesitate to seek medical care. They usually do not have unemployment benefits, and they cannot rely on their families, as some urban youths do, because their parents’ and grandparents’ pensions are “only enough to buy salt,

The other reality facing migrant workers is that returning to their villages to earn money farming is not an option, as Mr. Xi said it was. There is not enough land waiting for them

“For Chinese, especially in the countryside, there’s no such thing called retirement,” he said. His grandfather is 90 and cleans pig manure for a farm every day in the central province of Henan.

The morning we spoke he had just gotten off a shift that started at 7:30 p.m. and ended at 7 a.m. He had worked for two weeks without a day off because of the demand for Apple’s newest iPhone.

He feels he cannot go home to his village and do nothing while his parents and grandfather are still working. “It’s just not appropriate,”

“My ideal country is one where the people live in peace and prosperity, where there is food safety, freedom of speech, justice, a media that can expose injustices, a five-day, eight-hour workweek for workers,” said Mr. Zhang, the unemployed welder. “If these can be achieved, I will support whoever is in power, regardless of their party or how long they govern.”

Mr. Ge left his village at age 17 and started working on construction sites and in factories. He had benefits during the six years he worked at Foxconn, a contract manufacturer for Apple. But when he was out of work this year, he could not get any unemployment benefits, which is not uncommon as local governments are deeply in debt. Now 34, he still works 10-hour shifts at another Apple contract manufacturer and lives in a dormitory.

“Only people who couldn’t find jobs would do farming,” said Guan, a migrant worker in the northwestern province of Gansu, “because income from farming is too low.”

“To be honest, deep down I feel lost,” he said. “All I can say is that for the time being, I’ll save as much money as possible. As for what the future holds, it’s really hard to say. I might not even live to see that age.”

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.

Overall, 55 percent of Hispanic teenagers and 54 percent of Black teenagers say they are online almost constantly, compared with 38 percent of white teenagers;

Black and Hispanic kids ages 8 to 12, another study found, also use social media more than their white counterparts.

we also have to ask,” she went on, “why they are so drawn to social media? Is it the messages on social media that’s exacerbating the depression and anxiety, or was the depression and anxiety already there to begin with and social media is a way to self-medicate?”

“It’s culturally more acceptable in youth of color households to use technology for social and academic reasons compared with white households,” Charmaraman said. “Parents don’t worry as much about it. There isn’t as much shame around it.”

“We know broadly that youth of minoritized communities have longer commutes, fewer opportunities to do after-school activities, fewer resources,” Magis-Weinberg told me. They may not have spaces to hang out safely with friends nearby; social media is a more accessible option. “But we have to ask,” Magis-Weinberg added, “what is social media use displacing?”

Largely because of lower income levels, Black and Hispanic teenagers are less likely to have broadband access or computers at home. This makes them disproportionately use their smartphones, where social media apps ping, whiz and notify

Lucia Magis-Weinberg, an assistant professor of psychology at the University of Washington who studies teenagers and tech, compares internet use of the phone to snorkeling, whereas computers allow more of a scuba dive.

WhatsApp, hugely popular in Latin America, is used by Hispanic teenagers more than by other demographic groups of the same ages.

“The way social media use presents itself is as something that is actively harmful,” Marsh told me. Already kids from these communities have few advantages, he explained. They may not have access to after-school programs. They’re often in single-parent households. They lack support systems. “I think in the long term,” he said, “we’re going to see real differences in the impact.”

Let’s consider just reading, which also happens to be correlated with both mental well-being and school achievement

According to Scholastic’s most recent Kids and Family Reading Report, the percentage of kids ages 6 to 17 who read frequently for pleasure dropped to 28 percent in 2022 from 37 percent in 2010.

Those numbers fall precipitously as kids get older; 46 percent of 6- to 8-year-olds read frequently in 2022 compared with only 18 percent of 12- to 17-year-olds.

All this raises the possibility that disparities in internet use could in turn intensify overall declines and existing differences in reading across racial groups among adults.

The average daily time spent reading per capita by ethnicity in 2022 was 0.29 hours for white adults, 0.12 for Black adults and 0.10 for Hispanics.

In other words, one danger is that social media not only reflects real-world disparities, it could also exacerbate them.

Greater use of social media by Black and Hispanic young people “can help perpetuate inequality in society because higher levels of social media use among kids have been demonstrably linked to adverse effects such as depression and anxiety, inadequate sleep, eating disorders, poor self-esteem and greater exposure to online harassment,”

Akeem Marsh, medical director of the Home of Integrated Behavioral Health at the New York Foundling, a social services agency, said that among the hundreds of largely Black and Hispanic kids he sees from communities with fewer resources, social media use is often a primary concern or it comes up in treatment. Kids who use it frequently often respond with traumatized feelings and repeated anxiety.

The answer, according to experts, includes sports participation, in-person socializing, after-school clubs and activities, exploring the outdoors, reading and more.

We need greater awareness of the disparities as well, and most likely, immediate action. What we do not need is another “sudden” yet regrettably delayed realization that something has gone very, very wrong with America’s kids, but we were too busy looking the other way.

Things that were long big fun now overwhelm, exhaust and annoy. The holiday season is an extended exercise in excess and loud, often sleazy sweaters.

Which means it is nothing of the sort. This is the drag equivalent of fun and suggests that fun is done.

Adults assiduously record themselves appearing to have something masquerading as “fun,” a fusillade of Coachellic micro social aggressions unleashed on multiple social media platforms. Look at me having so much FUN!

“I feel like I should be having more fun than I’m actually having,” says Alyssa Alvarez, a social media marketing manager and DJ in Detroit, expressing a sentiment that many share. “There are expectations of what I want people to believe that my life is like rather than what my life is actually like.”

Weddings have morphed into multistage stress extravaganzas while doubling as express paths to insolvency: destination proposals for the whole family, destination bachelorette and bachelor blowouts, destination weddings in remote barns with limited lodging, something called a “buddymoon” (bring the gang!) and planners to help facilitate the same custom cocktailsness of it all.

What could be a greater cause for joy or more natural than having a baby? Apparently, not much these days. Impending parenthood is overthought and over-apped, incorporating more savings-draining events that didn’t exist a few decades ago: babymoons and lethal, fire-inducing, gender-reveal gatherings and baby showers so over-the-top as to shame weddings.

Retirements must be purposeful. Also, occasions for an acute identity crisis. You need to have a plan, a mission, a coach, a packed color-coded grid of daily activities in a culture where our jobs are our identities, our worth tied to employment.

Vacations are overscheduled with too many activities, FOMO on steroids, a paradox of choice-inducing decision fatigue, so much so that people return home exhausted and in need of another one.

“The world is so much less about human connection,” says Amanda Richards, 34, who works in casting in Los Angeles and is a graduate of Cudworth’s course. “We do more things virtually. People are more isolated. And there’s all this toxic positivity to convince people of how happy you are.”

For eons, early adulthood was considered an age of peak fun. Now, according to several studies, it’s a protracted state of anxiety and depression.

Because there is now a coach for everything, Alvarez hired the “party coach” Evan Cudworth, taking his $497 course this fall on how to pursue “intentional fun.” (It now costs $555.)

Instead of this being the most wonderful time of the year, we battle holiday fatigue, relentless beseeching for our money and, if Fox News is to be believed, a war on Christmas that is nearing its third decade.

Blame it on technological advances that tether us to work without cessation

Blame it on the pandemic, which exacerbated so much while delivering Zoomageddon.

Blame it on 2004, with the advent of Facebook, which led to Twitter (okay, X), Instagram, Threads, TikTok and who-knows-what lurking in the ether.

Blame it again on 2004 and the introduction of FOMO, our dread of missing out, broadcast through multiple social media spigots

“So many people are retreating into their phones, into anxiety,” says Cudworth, 37, from Chicago. “I’m helping people rediscover what fun means to them.”

His mandate is redefining fun: cutting back on bingeing screen time, eradicating envy scrolling, getting outside, moving, dancing. “With technology, we don’t allow ourselves to be present. You’re always thinking ‘something is better around the corner,’” Cudworth says, the now squandered in pursuit of the future.

Blame it on an American culture that values work, productivity, power, wealth, status and more work over leisure

ow do Americans spend their leisure hours when they might be having fun with others, making those vital in-person connections? Watching television, our favorite free time and “sports activity” (yes, that’s how it’s classified), according to the Bureau of Labor Statistics, an average of 2.8 hours daily.

“That’s way more television than you really need. We put play on the back burner,” says Pat Rumbaugh, 65, of Takoma Park, Md. She’s “The Play Lady,” who organizes unorganized play for adults

Catherine Price, the author of “The Power of Fun: How to Feel Alive Again,” believes “we’re totally misdoing leisure” and “not leaving any room for spontaneity.”

To Price, True Fun is the confluence of connection (other people, nature), playfulness (lightheartedness, freedom) and flow (being fully engaged, present), which is not as challenging as it sounds. “You can have fun in any context. Playfulness is about an attitude,”

Back in the day, co-workers were friends. (Sometimes, more.) After hours, they gathered for drinks, played softball. Today, because of email, Slack and remote work, offices are half empty and far quieter than libraries.

“We go to work and there’s no sense of connection and camaraderie,” says Davis, who was long employed by his city’s department of parks and recreation. “People feel emotionally disconnected. Healthy conversations are the precursor of fun. We’ve lost the art of communication. Our spirit comes home with us. If you don’t communicate at work, what are you coming home with?”

“Conservatives very much associate themselves now with the opposition to gun control, but that wasn’t always the case,” Blake Brown, a history professor at Saint Mary’s University in Halifax, told me. He said that Liberals and Conservatives passed firearms control measures in the 1950s and 1960s, and that both parties strengthened Canada’s gun laws in the years following the 1989 Montreal massacre.

As of May 18, Canadians who purchase a nonrestricted firearm, basically a rifle or shotgun, must provide identification as well as a valid firearms license. Businesses are required to keep these records, which may be viewed by the police with a warrant.

Two years ago, after the deadliest mass shooting in Canada’s history took place in Nova Scotia, his government banned military-style assault weapons. Last week, it carried out previously announced firearms record-keeping regulations.

“Certainly there have been periods in American history where they’ve been more aggressive in gun control than in Canada,” he said. “But, overall, the trend has been that Canada has seen themselves differently when it comes to firearms.” That has led to stricter gun laws amid fears of importing American gun violence.

It found that shotguns and rifles in Canada’s illegal market generally enter the system through legal purchases. That’s unlike illicit handguns, it said, which tend to be smuggled into Canada.

Since there is no systemic data collection on the origins of crime guns, one internal Statistics Canada presentation I read emphatically placed it in the “What we don’t know” category.

Without gun tracing and better data, the full picture of the United States’ effect on gun crimes in Canada will remain incomplete and based on haphazardly-tracked incidents like that of the gun-toting drone.

The effects of rising food prices have been felt unevenly around the world. Asia has been largely spared because of a plentiful rice crop. But parts of Africa, the Middle East and Latin America that are more dependent on imported food are struggling.

Joseph Siegle, the director of research at National Defense University’s Africa Center for Strategic Studies, estimated that 106 million people on the continent are facing food insecurity, double the number since 2018.

In the United States, food prices rose 6.3 percent in December compared with a year ago, while the price of restaurant meals rose 6.0 percent and the price of meat, poultry, fish and eggs jumped 12.5 percent, according to the Bureau of Labor Statistics.

But economists and agricultural experts say that while these efforts help at the margin, there may be little the government can do to combat a phenomenon that is both complex and global.

Overloaded shipping companies have been refusing to send their steel boxes to the Midwest to pick up agricultural products, instead preferring to ship them back to Asia to carry more lucrative cargo.

With both their costs and their sales prices increasing, many farmers are making similar margins to what they earned before, Mr. Edgington said. But “huge swings” in the price of corn, soybeans and fertilizer were still putting their finances at risk.

Caldwell’s words are worth remembering because they describe—perhaps more eloquently than anyone else in the west—not just why Putin built a following abroad, but also how he became (at least for a time) popular at home

He is the elected leader of Russia—a rugged, relatively poor, militarily powerful country that in recent years has been frequently humiliated, robbed, and misled. His job has been to protect his country’s prerogatives and its sovereignty in an international system that seeks to erode sovereignty in general and views Russia’s sovereignty in particular as a threat.

if we were to use traditional measures for understanding leaders, which involve the defense of borders and national flourishing, Putin would count as the pre-eminent statesman of our time. On the world stage, who can vie with him? Only perhaps Recep Tayyip Erdoğan of Turkey.

I’ve long thought about an argument in Victor Hugo’s Les Misérables

the available evidence indicates he’s been broadly popular in Russia for two decades. One wonders how long this popularity can hold as Russian forces struggle on the battlefield, but the bottom line is still clear—tens of millions of people (including some of the most influential people in the west) have admired an objectively evil man. Why? 

Throughout history we see familiar patterns, in times of stress and confusion, people cry out for salvation and strength. Success—including military success—builds a bond with the people. The victorious ruler connects not just with human pride, but also with profound human longings for protection, purpose, and identity.

One of the most fateful passages of scripture is found in 1 Samuel 8, the moment the people of Israel demand a king. Dissatisfied with the leadership of Samuel’s dissolute sons, they demanded a king “to judge us like all the nations.” 

“There shall be a king over us, that we also may be like all the nations, and that our king may judge us and go out before us and fight our battles.”

he did what Kemal Atatürk had done in Turkey in the 1920s. Out of a crumbling empire, he rescued a nation-state, and gave it coherence and purpose. He disciplined his country’s plutocrats. He restored its military strength. And he refused, with ever blunter rhetoric, to accept for Russia a subservient role in an American-run world system

Marius’s case for Napoleon feels relevant as Vladimir Putin longs for the glories of the Russian imperial past:

Putin inherited the husk of an empire, and in war after war, for year after year, he triumphed. He expanded the zone of Russian control. He replaced the lost Soviet identity with a renewed commitment to Russian greatness. While Putin’s victories weren’t as dramatic as Napoleon’s—Georgia and Crimea can’t compare to Jena and Austerlitz—war provides a terrible purpose, and victory creates remarkable pride. 

The sobering reality is that we cannot look at Russia and simply shake our heads at “those people,” secure in the knowledge that we’re fundamentally different

we’re still not far removed from a violent attempt to overturn an American election, led by a mob waving blue flags stamped with their leader’s name. 

There are those who see the ways in which human beings abuse liberty, or squander it in disorder and decadence, and are drawn to the order and direction of authoritarianism. In this formulation, freedom itself is the problem. It doesn’t provide purpose. 

The biggest factor making this kind of climate initiative possible, after so many years of inaction, is the spectacular technological progress in renewable energy that has taken place since 2009 or so. This means that we can greatly reduce emissions using carrots instead of sticks: giving people incentives to use low-emission technologies rather than trying to regulate or tax them into giving up high-emission activities.

the politics of carrots are obviously a lot easier than the politics of sticks.

Above all, real G.D.P. says nothing about how stuff is produced. A kilowatt-hour of electricity counts the same whether it was generated by burning coal or wind power, but the environmental impact is completely different.

So let’s talk about why such claims are all wrong.

many people don’t understand what economic growth means, imagining that it necessarily involves producing the same things you were producing before, in the same ways, but just at a larger scale.

But that’s not at all what growth means. Currently, America’s real gross domestic product is about a third larger than it was in 2007. But the economy of 2023 isn’t just the economy of 2007 scaled up by a third. Production of some goods has gone way down — coal production has been cut roughly in half.

at this precise moment — the most hopeful moment for the environment, as far as I can tell, in decades — my inbox has been filling up with woeful claims that environmental protection is incompatible with economic growth. These claims are oddly bipartisan. Some of them come from people on the left who insist that the planet can’t be saved unless we give up on the notion of perpetual economic growth. Others come from people on the right who insist that we must give up on all this environmentalism if we want to preserve prosperity.

In fact, environmental quality is often better in rich countries, with high G.D.P. per capita, than in middle-income countries

As a result, there’s no reason a growing economy must place an increasing burden on the environment.

the environmental Kuznets curve.

, a comparison between the New York metropolitan area and Delhi, India. Delhi has a larger population but a much smaller G.D.P. So does New York’s big economy mean a highly stressed environment?

how does air quality in the two cities compare? As anyone who’s visited both places knows, New York air is, well, relatively OK, while Delhi air … isn’t.

at higher levels of development, delinking growth from environmental impact isn’t just possible in principle but something that happens a lot in practice.

Here’s a favorite chart of mine from the invaluable Our World in Data website. It shows carbon dioxide emissions per capita in Britain, where the Industrial Revolution began. The early phases of industrialization were indeed associated with a huge rise in emissions. But more recently emissions have fallen back to the levels of the ’50s — the 1850s:

How did Britain do that? Part of the answer is that over time the British economy switched from relying on coal to relying on hydrocarbons, which when burned generate less carbon dioxide. Britain also learned to use energy more efficiently over time. But more recently a big factor has been the rise of renewable energy, especially, in Britain’s case, wind power

So when you hear an environmentalist say something like, “We live on a finite planet, so we can’t have unlimited economic growth,” what they’re actually revealing is that they don’t understand what economic growth means

in practice, they’re lending aid and comfort to anti-environmentalists, who want us to believe that protecting the environment is incompatible with rising living standards.

That said, while it’s possible to decouple growth from environmental harm, that’s not automatic. To combine rising living standards with an improving environment, we need policies that encourage the use of technologies that cause less environmental damage.

The good news is that the United States is finally implementing such policies. Still, we need a lot more action along those lines — not just in America but in the rest of the world. So we can do this — but we need to try, and not give in to counsels of despair.

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.”

Others point to driving’s high cost. Car insurance has skyrocketed in price in recent years, increasing nearly 14 percent between 2022 and 2023. (The average American now spends around 3 percent of their yearly income on car insurance.) Used and new car prices have also soared in the last few years, thanks to a combination of supply chain disruptions and high inflation.

E-scooters, e-bikes and ride-sharing also provide Gen Zers options that weren’t available to earlier generations. (Half of ride-sharing users are between the ages of 18 and 29, according to a poll from 2019.) And Gen Zers have the ability to do things online — hang out with friends, take classes, play games — which used to be available only in person.

Whether this shift will last depends on whether Gen Z is acting out of inherent preferences, or simply postponing key life milestones that often spur car purchases. Getting married, having children, or moving out of urban centers are all changes that encourage (or, depending on your view of the U.S. public transit system, force) people to drive more.

In 1997, 43 percent of 16-year-olds and 62 percent of 17-year-olds had driver’s licenses. In 2020, those numbers had fallen to 25 percent and 45 percent.

Millennials went through a similar phase. Around a decade ago, many newspaper articles and research papers noted that the millennial generation — often defined as those born between 1981 and 1996 — were shunning cars. The trend was so pronounced that some researchers dubbed millennials the “go-nowhere” generation.

The average number of vehicle miles driven by young people dropped 24 percent between 2001 and 2009, according to a report from the Frontier Group and the U.S. Public Interest Research Group. And at the same time, vehicle miles traveled per person in the United States — which had been climbing for more than 50 years — began to plateau.

adult millennials continue to drive around 8 percent less every day than members of Generation X and baby boomers. As millennials have grown up, got married and had kids, the distance they travel in cars has increased — but they haven’t fully closed the gap with previous generations.

data has shown that U.S. car culture isn’t as strong as it once was. “Up through the baby boom generation, every generation drove more than the last,” Dutzik said. Forecasters expected that trend to continue, with driving continuing to skyrocket well into the 2030s. “But what we saw with millennials, I think very clearly, is that trend stopped,”

If Gen Zers continue to eschew driving, it could have significant effects on the country’s carbon emissions. Transportation is the largest source of CO2 emissions in the United States. There are roughly 66 million members of Gen Z living in the United States. If each one drove just 10 percent less than the national average — that is, driving 972 miles less every year — that would save 25.6 million metric tons of carbon dioxide from spewing into the atmosphere. That’s the equivalent to the annual emissions of more than six coal-fired power plants.

two uncomfortable facts

First, a number of influential people, both in business and in politics, are deeply financially enmeshed with Russian kleptocrats. This is especially true in Britain.

Second, it will be hard to go after laundered Russian money without making life harder for all money launderers, wherever they come from — and while Russian plutocrats may be the world champions in that sport, they’re hardly unique: Ultrawealthy people all over the world have money hidden in offshore accounts.

It has become an argument over a nominee who does not exist — one who does not respect America, is not truly religious, coddles child abusers and terrorists and has highly developed views about the importance of “woke” education.

Conspiracy theories and unfair accusations have a long history in American politics, of course. But they have often remained on the margins. Today, distortions and falsehoods have moved to the center of politics.

While neither party is entirely innocent, there is a fundamental difference between Republicans and Democrats. False claims regularly flow from the leaders of the Republican Party —

The Jackson hearings have become the latest example. Several Republican senators — including Josh Hawley, Lindsey Graham and Ted Cruz yesterday — have tried to portray her as soft on child pornographers. Their argument depends on a misleading cherry-picking of facts from cases she has heard.

Woke education has become another focus of the hearings, with Republicans like Cruz and Marsha Blackburn trying to portray Jackson as an advocate for it. In truth, she has not taken a position on the issues that fall under that category.

That was apparently enough for the Republican National Committee to tweet an image of her this week, with her initials — KBJ — crossed out and replaced with CRT, an abbreviation for critical race theory.

The only time Jackson appears to have mentioned critical race theory publicly was in a 2015 speech. It was part of a list of disciplines that she said had an intellectual connection to criminal sentencing, including administrative law, philosophy, psychology and statistics.

There is one broader political risk here for Democrats, though: imagining that Republicans are simply playing to their base by making these misleading criticisms of Jackson. They are also trying to appeal to swing voters.

Most Americans oppose cutting police budgets, for instance. Many believe that allowing all transgender girls to compete in girls’ sports can be unfair to other girls. Many voters — and not just white voters — think that liberals focus too much on racial identity.

“One thing that is striking about this hearing,” Lori Ringhand, a legal scholar, told The Times, “is how little effort we are seeing to engage the nominee on her views about actual legal issues.”

This is a delayed but inescapable consequence of China’s birth trends from the era of the notorious one-child policy (1980-2015)

Beijing thus far has ignored this looming crisis because planners don’t prepare for things they don’t track. Officials don’t regard data on the family as relevant to statecraft or security. So statistics tally males and females—not uncles, sisters, cousins, widows.

We estimate past patterns and project trends through demographic modeling—simulations replicating China’s available population numbers—while “building” family trees consistent with those figures. We can approximate nationwide changes in China’s extended family networks in the past with reasonable validity and describe what lies ahead with fair confidence.

we are only now living through the era of “peak kin” in China. In terms of sheer numbers, Chinese networks of blood relatives were never nearly as thick as at the start of the 21st century.

Because of dramatic postwar improvements in health and mortality, men and women in their 40s today have on average five times as many living cousins as in 1960.

China’s “kin explosion” may be an important, heretofore unobserved factor in China’s remarkable economic performance since Mao Zedong’s death in 1976.

China is now on the cusp of a severe and unavoidable “kin crash,” driven by prolonged subreplacement fertility

China’s rising generations will likely have fewer living relatives than ever before in Chinese history.

A “kin famine” will thus unfold unforgivingly over the next 30 years—starting now. As it intensifies, the Chinese family—the most important institution protecting Chinese people against adversity in bad times and helping them seize opportunity in good times—will increasingly falter in both these crucial functions.

China’s withering of the family is set to collide with a tsunami of new social need from the country’s huge elderly population, whose ranks will more than double between 2020 and 2050

By 2050 living parents and in-laws will outnumber children for middle-aged Chinese men and women. Thus exigency may overturn basic familial arrangements that have long been taken for granted. The focus of the family in China will necessarily turn from the rearing of the young to the care of the old.

The reliability and durability of familial bonds of duty will be an increasingly critical question—perhaps even a matter of life and death for many, including frail and impecunious elders in the Chinese hinterlands

growing numbers of men in decades ahead will enter old age without spouses or children—the traditional sources of support for the elderly.

by 2050, 18% of China’s men in their 60s will have no living descendants, twice the fraction today.

who will look after these unfortunates?

Still worse than the macroeconomic implications of old-age dependency may be the effect of China’s family crisis on the so-called micro-foundations of the national economy—the little things that make markets work.

Since earliest recorded history, China’s guanxi networks, a distinctive form of special relationships and professional connections, have helped get business done by reducing uncertainty and transaction costs. The proliferation of blood relatives was likely a powerful stimulant for growth during the era of China’s phenomenal upswing.

the kin dearth may prove an economic depressant well beyond what current “head count” projections suggest.

China’s coming family revolution could easily conduce to a rise in personal risk aversion. Risk aversion may in turn dampen mobility, including migration.

Less migration means less urbanization, which means less growth—and possibly still more pessimism and risk aversion.

If the waning of the family requires China to build a huge social welfare state over the coming generation, as we surmise it will, Beijing would have that much less wherewithal for influencing events abroad through economic diplomacy and defense policy.

by 2050 at least half of China’s overall pool of male military-age manpower will be made up of only children. Any encounter by China’s security forces involving significant loss of life will presage lineage extinction for many Chinese families.

Autocracies are typically tolerant of casualties—but maybe not in the only-child China of today and the decades ahead.

Failure to contemplate the implications of the coming changes in Chinese family structure could prove a costly blind spot for the Communist Party. Blind spots expose governments to the risk of strategic surprise. The consequences of social, economic and political risks tend to be greatest when states aren’t prepared for them.

the most explosive growth has been in Indigenous, independent Pentecostal churches

we ought to start talking about a new family of “spiritual” churches that have no historical ties to Western church traditions. These “spiritual” churches are largely not a result of colonial missions.

we tend to associate Christianity with white Westerners and European influence. At this point, our assumptions about this need to change. The largest church congregation in the world belongs to Yoido Full Gospel Church in Seoul, an Assemblies of God church, which has around 480,000 members

even conservative estimates guess there were around 98 million evangelical Christians globally in 1970. Now, there are over 342 million.

as Christianity booms overseas, more Christians are migrating to the United States. But there’s also evidence that migrants who come to here are finding immigrant-led churches and converting to Christianity after they arrive. These trends, George told me, are “globalizing American Christianity.”

Today, the three largest Protestant churches in Paris are Afro-Caribbean evangelical megachurches of a charismatic or Pentecostal bent

Immigration has been a huge factor in the demographic growth of the United States over the past decade. Much of that growth is attributable to Latinos, who now number around 62 million and represent just under 20 percent of the United States population. Some projections estimate that by 2060 there will be 111 million Latinos in the United States, constituting 28 percent of the population.

Latino evangelicals are the fastest growing segment of evangelicals in the country. It also said that “Latino Protestants, in particular, have higher levels of religiosity”

as of 2021, Pew reported that 29 percent of all adults identified as atheists, agnostics or “nothing in particular.”

alongside that trend, the changing demographics of Christianity promise to transform faith and religious discourse. We cannot assume that America will become more secular so long as the future of America is less white

it is difficult to provide definite statistics on how many evangelical and Pentecostal churches in America are led by Latinos, immigrants or other nonwhite or non-English-speaking pastors because many of these churches are small, non-organized and grass roots.

the standard American religious survey categories no longer account for the realities expressed in the church in America. “White evangelicalism,” “Protestant mainline” and “progressive” are categories that are largely defined by a white majority.

This “browning” of the church in America, as some scholars call it, scrambles all the categories. What we are seeing isn’t simply that white evangelicalism is changing; it’s that something new is emerging.

most hold convictions that overlap with traditional evangelicalism in substantial ways. They are by and large traditionally conservative about sexuality and marriage. They hold an authoritative view of the Bible and believe in miracles and supernatural occurrences.

But they tend to be more committed to social justice and, in George’s words, “communitarian” than many white evangelicals.

when he visits churches in Brazil and Argentina, “Sometimes the Catholics are more evangelical and Pentecostal” than even typical white evangelicals in America.

This influx of nonwhite believers will challenge white religious conservatives to choose between xenophobia and building alliances with immigrants who share their views on social issues.

These trends will also challenge them to unbundle their religious views on social issues from a kind of libertarian economics that harms those who are less wealthy

The future of American Christianity now appears to be a multiethnic community that is largely led by immigrants or the children of immigrants. And that reality ought to change our present conversations about religion in America.

How could these two seemingly contradictory statements both be true?

respondents in 2016 and 2020 reported more moderate views, on average, than in previous elections. As a result, Trump improved the most over previous Republicans by capturing the votes of a larger number of people who report racially moderate views

the number of people scoring at moderate levels of racial resentment increased. Trump was not as popular among this voting bloc, compared to those with high racial resentment. But because this group is larger, whites with moderate racial resentment scores ended up contributing more net votes to Trump.

The point about Trump voters being less racially resentful on average than voters for previous Republican candidates, while likely true, should, I think, be interpreted as a statement about why it’s important to be mindful of over-time changes in groups’ sizes in the population,