Group items tagged

A) is very bright; and B) would solidify a conservative Supreme Court majority whose judicial philosophy has been on the wrong side of many of the great issues of my lifetime.

that this path toward social progress would ideally have been blazed by legislators, not judges.

Forward-thinking justices struck down such laws — and that wasn’t about “activist judges” but about decency, humanity and the 14th Amendment.

Three backward-thinking justices, including Antonin Scalia, Barrett’s mentor, would have allowed Taliban-style prosecutions of gay people for intimacy in the bedroom. (Barrett refused in the hearing Wednesday to say whether the case was rightly decided.)

Perhaps the divide instead is between forward-thinking judges and backward-thinking judges.

That’s one reason next month’s election is such a milestone, for one political party in America is trying to join the rest of the civilized world and provide universal health care, and the other is doing its best to take away what we have.

My take is that Democrats are exaggerating that risk; the Republican argument in the case, to be heard next month, is such a legal stretch that it’s unlikely to succeed fully, even if Barrett is on the court.

Yet she’s working with a gang of Republican senators to steal a seat on the Supreme Court.

Will voters reward the party that is working to provide more health care, or the party that has painstakingly robbed one million children of insurance? Will voters help tug the United States forward, or will they support the backward thinkers who have been on the side of discrimination, racism, bigotry and voter suppression?

which side of history will you stand on?

The Mexican author Octavio Paz, in his 1950 book, The Labyrinth of Solitude, described the pachuco (a word used to refer to young Mexican American men, many of them gang members, in the mid-1900s) as a “pariah, a man who belongs nowhere,” alienated from his Mexican roots but not quite of the United States either.

Gloria Anzaldúa, in her 1987 classic, Borderlands/La Frontera, described Chicana identity as the product of life along the U.S.-Mexico border, “una herida abierta [an open wound] where the Third World grates against the first and bleeds.”

We need to understand that they want the same freedoms, comforts, and securities that all people have wanted since the beginning of civilization: to have a “home with a place to paint, or a big, comfortable chair to sit in and read under a lamp, with a cushion under the small of our backs.”

offers a more intimate look into the barrios, homes, and minds of people who, he argues, have been badly, and sometimes willfully, misunderstood.

Tobar’s main focus is on how the migrant experience has shaped Latino identity.

More than these other works, though, it engages in contemporary debates and issues, such as how Latinos have related to Blackness and indigeneity, the question of why some Latinos choose to identify as white, and the political conservatism of certain Latino communities

“To be Latino in the United States,” Tobar writes, “is to see yourself portrayed, again and again, as an intellectually and physically diminished subject in stories told by others.”

Even when migrants survive the journey and settle across the United States, Tobar sees a dark thread connecting them: “Our ancestors,” he writes, “have escaped marching armies, coups d’état, secret torture rooms, Stalinist surveillance, and the outrages of rural police forces.” Tobar is referring here to the domestic conflicts, fueled by the U.S. military, in Guatemala, Cuba, El Salvador, Nicaragua, and other countries during the Cold War, leading to unrest and forcing civilians in those places to flee northward.

For Tobar, this history of violence is something all Latinos have in common, no matter where in the country they live.

He writes, “I want a theory of social revolution that begins in this kind of intimate space,” not in the symbols “appropriated by corporate America,” like the Black Lives Matter banners displayed at professional sporting events, or the CEO of JPMorgan Chase kneeling at a branch of his bank, which critics have read as virtue signaling. Mere intimacy and the recognition of common histories isn’t the same as justice, but it is a necessary starting point for healing divisions.

there are many Latino stories that he does not, and probably cannot, tell. For one, he conceives of Latino history as the history of a people who have endured traumas because of the actions of the U.S. But this framing wouldn’t appeal to Latinos who see the United States as the country where their dreams came true, where they’ve built careers, bought homes, provided for their families.

If the small number of conservative Latinos Tobar interviewed are anything like the Hispanic Republicans I’ve talked with over the years, they would tell him that it is the Republican Party that best represents their economic, religious, and political values.

If our aim is to understand the full story of Latinos—assuming such a thing is possible—we should explore all of the complexities of those who live in a country that is becoming more Latino by the day. For that, we’ll need other books besides Our Migrant Souls, ones that describe the inner worlds, motives, and ambitions of Latinos who see themselves and their place in this country differently.

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.

A chat app can be social like a huddle of people in focused conversation. Participants are speaking to be heard by one another, not by the entire world. There is more concentration and potential for intimacy, and much less performance.

For Ukrainians and Russians resisting the war, the different ways of engaging through Telegram—receiving information through dedicated channels, forming groups committed to one topic, or chattering in hushed tones—benefit a people that will likely have to sustain a long insurgency.

It was, Salem wrote, “group-think on steroids—an abomination of a monster with thousands of arms and no brain.”

Chat apps are merely the latest expression of the smaller, more controlled spaces that have always been central to incubating new ideas or challenging existing ones.

in the Soviet Union, samizdat played this role. The underground, illegal, and self-produced writing, passed hand to hand, kept alive a shadow civil society during repressive periods.

The lineage of such underground communication stretches across history, from the 17th-century scholars in Europe who collaborated through letters on scientific experiments in defiance of Church doctrine all the way to the xeroxed and stapled zines of the 1990s, which set the tone of third-wave feminism.

What matters is that embattled Ukrainians and anti-war Russians are attempting to practice privacy and quiet communication—necessary virtues for a resistance movement.

Parents need to know that when children go online, they are entering a world of influencers, many of whom are hoping to make money by pushing dangerous products. It’s a world that’s invisible to us

when we log on to our social media, we don’t see what they see. Thanks to algorithms and ad targeting, I see videos about the best lawn fertilizer and wrinkle laser masks, while Ian is being fed reviews of flavored vape pens and beautiful women livestreaming themselves gambling crypto and urging him to gamble, too.

Smartphones are taking our kids to a different world

Greyson Imm, an 18-year-old high school student in Prairie Village, Kan., said he was 17 when Zyn videos started appearing on his TikTok feed. The videos multiplied through the spring, when they were appearing almost daily. “Nobody had heard about Zyn until very early 2023,” he said. Now, a “lot of high schoolers have been using Zyn. It’s really taken off, at least in our community.”

all of this is, unfortunately, only part of what makes social media dangerous.

The tobacco conglomerate Philip Morris International acquired the Zyn maker Swedish Match in 2022 as part of a strategic push into smokeless products, a category it projects could help drive an expected $2 billion in U.S. revenue in 2024.

P.M.I. is also a company that has long denied it markets tobacco products to minors despite decades of research accusing it of just that. One 2022 study alone found its brands advertising near schools and playgrounds around the globe.

the ’90s, when magazines ran full-page Absolut Vodka ads in different colors, which my friends and I collected and taped up on our walls next to pictures of a young Leonardo DiCaprio — until our parents tore them down. This was advertising that appealed to me as a teenager but was also visible to my parents, and — crucially — to regulators, who could point to billboards near schools or flavored vodka ads in fashion magazines and say, this is wrong.

Even the most committed parent today doesn’t have the same visibility into what her children are seeing online, so it is worth explaining how products like Zyn end up in social feeds

influencers. They aren’t traditional pitch people. Think of them more like the coolest kids on the block. They establish a following thanks to their personality, experience or expertise. They share how they’re feeling, they share what they’re thinking about, they share stuff they l

With ruthless efficiency, social media can deliver unlimited amounts of the content that influencers create or inspire. That makes the combination of influencers and social-media algorithms perhaps the most powerful form of advertising ever invented.

Videos like his operate like a meme: It’s unintelligible to the uninitiated, it’s a hilarious inside joke to those who know, and it encourages the audience to spread the message

Enter Tucker Carlson. Mr. Carlson, the former Fox News megastar who recently started his own subscription streaming service, has become a big Zyn influencer. He’s mentioned his love of Zyn in enough podcasts and interviews to earn the nickname Tucker CarlZyn.

was Max VanderAarde. You can glimpse him in a video from the event wearing a Santa hat and toasting Mr. Carlson as they each pop Zyns in their mouths. “You can call me king of Zynbabwe, or Tucker CarlZyn’s cousin,” he says in a recent TikTok. “Probably, what, moved 30 mil cans last year?”

Freezer Tarps, Mr. VanderAarde’s TikTok account, appears to have been removed after I asked the company about it. Left up are the large number of TikToks by the likes of @lifeofaZyn, @Zynfluencer1 and @Zyntakeover; those hashtagged to #Zynbabwe, one of Freezer Tarps’s favorite terms, have amassed more than 67 million views. So it’s worth breaking down Mr. VanderAarde’s videos.

All of these videos would just be jokes (in poor taste) if they were seen by adults only. They aren’t. But we can’t know for sure how many children follow the Nelk Boys or Freezer Tarps — social-media companies generally don’t release granular age-related data to the public. Mr. VanderAarde, who responded to a few of my questions via LinkedIn, said that nearly 95 percent of his followers are over the age of 18.

They’re incentivized to increase their following and, in turn, often their bank accounts. Young people are particularly susceptible to this kind of promotion because their relationship with influencers is akin to the intimacy of a close friend.

The helicopter video has already been viewed more than one million times on YouTube, and iterations of it have circulated widely on TikTok.

YouTube said it eventually determined that four versions of the Carlson Zyn videos were not appropriate for viewers under age 18 under its community guidelines and restricted access to them by age

Mr. Carlson declined to comment on the record beyond his two-word statement. The Nelk Boys didn’t respond to requests for comment. Meta declined to comment on the record. TikTok said it does not allow content that promotes tobacco or its alternatives. The company said that it has over 40,000 trust and safety experts who work to keep the platform safe and that it prevented teenagers’ accounts from viewing over two million videos globally that show the consumption of tobacco products by adults. TikTok added that in the third quarter of 2023 it proactively removed 97 percent of videos that violated its alcohol, tobacco and drugs policy.

Greyson Imm, the high school student in Prairie Village, Kan., points to Mr. VanderAarde as having brought Zyn “more into the mainstream.” Mr. Imm believes his interest in independent comedy on TikTok perhaps made him a target for Mr. VanderAarde’s videos. “He would create all these funny phrases or things that would make it funny and joke about it and make it relevant to us.”

It wasn’t long before Mr. Imm noticed Zyn blowing up among his classmates — so much so that the student, now a senior at Shawnee Mission East High School, decided to write a piece in his school newspaper about it. He conducted an Instagram poll from the newspaper’s account and found that 23 percent of the students who responded used oral nicotine pouches during school.

“Upper-decky lip cushions, ferda!” Mr. VanderAarde coos in what was one of his popular TikTok videos, which had been liked more than 40,000 times. The singsong audio sounds like gibberish to most people, but it’s actually a call to action. “Lip cushion” is a nickname for a nicotine pouch, and “ferda” is slang for “the guys.”

“I have fun posting silly content that makes fun of pop culture,” Mr. VanderAarde said to me in our LinkedIn exchange.

I turned to Influencity, a software program that estimates the ages of social media users by analyzing profile photos and selfies in recent posts. Influencity estimated that roughly 10 percent of the Nelk Boys’ followers on YouTube are ages 13 to 17. That’s more than 800,000 children.

I’ve spent the past three years studying media manipulation and memes, and what I see in Freezer Tarps’s silly content is strategy. The use of Zyn slang seems like a way to turn interest in Zyn into a meme that can be monetized through merchandise and other business opportunities.

Such as? Freezer Tarps sells his own pouch product, Upperdeckys, which delivers caffeine instead of nicotine and is available in flavors including cotton candy and orange creamsicle. In addition to jockeying for sponsorship, Mr. Carlson may also be trying to establish himself with a younger, more male, more online audience as his new media company begins building its subscriber base

This is the kind of viral word-of-mouth marketing that looks like entertainment, functions like culture and can increase sales

What’s particularly galling about all of this is that we as a society already agreed that peddling nicotine to kids is not OK. It is illegal to sell nicotine products to anyone under the age of 21 in all 50 states

numerous studies have shown that the younger people are when they try nicotine for the first time, the more likely they will become addicted to it. Nearly 90 percent of adults who smoke daily started smoking before they turned 18.

Decades later — even after Juul showed the power of influencers to help addict yet another generation of children — the courts, tech companies and regulators still haven’t adequately grappled with the complexities of the influencer economy.

Facebook, Instagram and TikTok all have guidelines that prohibit tobacco ads and sponsored, endorsed or partnership-based content that promotes tobacco products. Holding them accountable for maintaining those standards is a bigger question.

We need a new definition of advertising that takes into account how the internet actually works. I’d go so far as to propose that the courts broaden the definition of advertising to include all influencer promotion. For a product as dangerous as nicotine, I’d put the bar to be considered an influencer as low as 1,000 followers on a social-media account, and maybe if a video from someone with less of a following goes viral under certain legal definitions, it would become influencer promotion.

Laws should require tech companies to share data on what young people are seeing on social media and to prevent any content promoting age-gated products from reaching children’s feeds

hose efforts must go hand in hand with social media companies putting real teeth behind their efforts to verify the ages of their users. Government agencies should enforce the rules already on the books to protect children from exposure to addictive products,

I refuse to believe there aren’t ways to write laws and regulations that can address these difficult questions over tech company liability and free speech, that there aren’t ways to hold platforms more accountable for advertising that might endanger kids. Let’s stop treating the internet like a monster we can’t control. We built it. We foisted it upon our children. We had better try to protect them from its potential harms as best we can.

All the old deficiencies and blind spots of male socialization are still in circulation — the same mass failure to teach boys relational skills and emotional intelligence, the same rigid masculinity norms and social prohibitions that push them away from intimacy and emotionality.

in many ways this environment has apparently had the opposite effect — it has shut them down even further.

The micro-generation that was just hitting puberty as the #MeToo movement exploded in 2017 is now of college (and voting) age. They have lived their whole adolescence not just in the digital era, with a glorious array of virtual options to avoid the angst of real-world socializing, but also in the shadow of a wider cultural reckoning around toxic masculinity.

We have spent the past half-decade wrestling with ideas of gender and privilege, attempting to challenge the old stereotypes and power structures. These conversations should have been an opportunity to throw out the old pressures and norms of manhood, and to help boys and men be more emotionally open and engaged.

in screen-addicted, culture war-torn America, we have also added new ones.

For many progressives, weary from a pileup of male misconduct, the refusal to engage with men’s feelings has now become almost a point of principle

For every right-wing tough guy urging his crying son to “man up,” there’s a voice from the left telling him that to express his concerns is to take airtime away from a woman or someone more marginalized

In many cases, the same people who are urging boys and men to become more emotionally expressive are also taking a moral stand against hearing how they actually feel

For many boys, it can seem as though their emotions get dismissed by both sides. This political isolation has combined with existing masculine norms to push a worrying number of boys into a kind of resentful, semi-politicized reclusion.

Over a quarter of men under 30 say they have no close friends

Teenage boys now spend two hours less a week socializing than girls and they also spend about seven hours more per week than their female peers on screens.

my own research has fed my fears.

the same theme came up over and over for boys who on the face of it had little else in common. They were lonely.

almost all of them had the nagging sense that something important was missing in those friendships. They found it almost impossible to talk to their male peers about anything intimate or express vulnerability.

One teenager described his social circle, a group of boys who had been best friends since kindergarten, as a “very unsupportive support system.” Another revealed that he could recall only one emotionally open conversation with a male friend in his life, and that even his twin brother had not seen him cry in years

they felt unable to articulate this pain or seek help, because of a fear that, because they were boys, no one would listen.

As one 20-year-old put it, “If a man voices any concern, they get deflected with all of their so-called privileges.” He added: “They’d be like, ‘Whatever. Women have suffered more than you, so you have no right to complain.’”

Almost without exception, the boys I talked to craved closer, more emotionally open relationships, but had neither the skills nor the social permission to change the story.

Perhaps it’s not surprising that boys don’t know how to listen and engage with their friends’ emotions on any deeper level; after all, no one really engages with theirs.

in a sexist society, male opinions hold outsized value. But the world — including their own parents — has less time for their feelings.

One study from 2014 showed that parents were more likely to use emotional words when talking with their 4-year-old daughters than those speaking to their 4-year-old sons.

A more recent study comparing fathers of boys with fathers of girls found that fathers of boys were less attentively engaged with their boys, spent less time talking about their son’s sad feelings and instead were more likely to roughhouse with them. They even used subtly different vocabularies when talking with boys, with fewer feelings-centered words, and more competition and winning-focused language.

Spend any time in the manosphere, and it’s easy to start to hate men and boys. The extreme misogyny, the gleeful hate speech, the violent threats and thrum of menace make it hard to summon much sympathy for male concerns, and easy to forget the ways that patriarchy harms them, too.

in the grip of the culture wars, caring about boys has become subtly coded as a right-wing cause,

Men have had way more than their fair share of our concern already, the reasoning goes, and now it’s time for them to pipe down

But for boys, privilege and harm intertwine in complex ways — male socialization is a strangely destructive blend of indulgence and neglect. Under patriarchy, boys and men get everything, except the thing that’s most worth having: human connection.

The prescription for creating a generation of healthier, more socially and emotionally competent men is the same in the wider political discourse as it is in our own homes — to approach boys generously rather than punitively

We need to acknowledge boys’ feelings, to talk with our sons in the same way we do our daughters, to hear them and empathize rather than dismiss or minimize, and engage with them as fully emotional beings.