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The pressure on prices has been significant. Continue reading the main story Related Coverage China’s Slowdown Tarnishes Economic Boom in Copper-Rich ZambiaDEC. 2, 2015 A Global Chill in Commodity Demand Hits America’s HeartlandOCT. 23, 2015 Glencore Isn’t Out of the Woods YetOCT. 1, 2015 Prices for iron ore, the crucial steelmaking ingredient, have fallen by about 40 percent this year. The Brent crude oil benchmark is now hovering around $40 a barrel, down from more than a $110 since the summer of 2014.

Companies are caught in the downdraft.A number of commodity-related businesses have either declared bankruptcy or fallen behind in their debt payments

Even more common are the cutbacks. Nearly 1,200 oil rigs, or two-thirds of the American total, have been decommissioned since late last year. More than 250,000 workers in the oil and gas industry worldwide have been laid off, with more than a third coming in the United States.

The international mining company Anglo American is pulling back broadly, with a goal to reduce the company’s size by 60 percent. Along with the layoffs announced on Tuesday, the company is suspending its dividend, halving its business units, as well as unloading mines and smelters.

China looms large in the commodities equation.Between 2000 and last year, companies invested hundreds of billions of dollars to expand their production capacity to satisfy China in a period of rapid economic expansion. Much of the corporate growth was fueled by debt.

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.

But environmentalists say that mining is still a cause for concern particularly because miners will go wherever electricity is cheapest and that may mean places that use coal. According to Cambridge, China has the most bitcoin mining of any country by far. While the country has been slowly moving toward renewable energy, about two-thirds of its electricity comes from coal.

A single transaction of bitcoin has the same carbon footprint as 680,000 Visa transactions or 51,210 hours of watching YouTube, according to the site.

“Computers and smartphones have much larger carbon footprints than typewriters and telegraphs. Sometimes a technology is so revolutionary and important for humanity that society accepts the tradeoffs,” wrote investor Tyler Winklevoss on Twitter.

Vitalik Buterin, the computer scientist who invited ethereum, told IEEE Spectrum that mining cryptocurrency can be “a huge waste of resources, even if you don’t believe that pollution and carbon dioxide are an issue”, Buterin said. “There are real consumers – real people – whose need for electricity is being displaced by this stuff.”

Climate change isn’t the only problem. Cocoa crops in Côte d’Ivoire and Ghana, where 60 percent of the world’s cocoa come from, may already be in “structural decline,” Debenham said, citing disease, aging cocoa trees, and illegal gold mining on farmland.

ore important, the farmers who tend to the crops can’t afford to invest in their farms to increase their yields and bolster resilience against climate change. The bleak outlook for cocoa farmers threatens to doom cocoa-growing in the region altogether. In Ghana, the average cocoa farmer is close to 50 years old. A new generation of farmers is needed to maintain the cocoa supply, but young people may just walk away from the industry.

Newer chocolate alternatives may provide more satisfying counterfeits. Win-Win isn’t the only start-up producing cocoa-free chocolate, which is similar in concept to animal-free meat. The company uses plant ingredients to emulate the flavor and texture of chocolate—as do its competitors Foreverland and Voyage Foods. Another firm, California Cultured, grows actual cacao cells in giant steel tanks.

Cocoa shortages will affect all kinds of chocolate, but mass-produced sweets may change beyond just the prices. The erratic temperatures brought about by climate change could change the flavor of beans, depending on where they are grown

Variability is a concern for commercial chocolate makers, who need to maintain consistent flavors across their products. They may counteract discrepancies among different batches of beans by combining them, then roasting them at a higher temperature,

Commercial chocolate makers may also tweak their recipes to amp up or mimic chocolate flavors without using more cocoa. These candies contain relatively little cacao to begin with; only 10 percent of a product’s weight must be cocoa in order to qualify as chocolate in the eyes of the FDA.

No matter how you look at it, the future of cocoa doesn’t look good. With less cocoa available all around, chocolate may become more expensive. For high-end chocolate brands, whose products use lots of cocoa, the recent price hikes are reportedly an existential threat.

So much of the appeal of cheap chocolate is that it’s always been there—whether in the form of a Hershey’s Kiss, Oreo cookies, a bowl of Cocoa Puffs, or the shell of a fondant-filled egg. “You grow up with those tastes. It’s hard to fathom how pervasive it has been,” Carodenuto said. Chocolate lovers have weathered minor tweaks to these candies over the years, but the shifts happening today may be less tolerable—or at the very least more noticeable. The change that has been hardest to ignore is that cheap chocolate is no longer that cheap.

Have such technological leaps been good? Harari has doubts. Humans have “produced little that we can be proud of,” he complained in Sapiens. His next books, Homo Deus: A Brief History of Tomorrow (2015) and 21 Lessons for the 21st Century (2018), gazed into the future with apprehension

Harari has written another since-the-dawn-of-time overview, Nexus: A Brief History of Information Networks From the Stone Age to AI. It’s his grimmest work yet

Harari rejects the notion that more information leads automatically to truth or wisdom. But it has led to artificial intelligence, whose advent Harari describes apocalyptically. “If we mishandle it,” he warns, “AI might extinguish not only the human dominion on Earth but the light of consciousness itself, turning the universe into a realm of utter darkness.”

Those seeking a precedent for AI often bring up the movable-type printing press, which inundated Europe with books and led, they say, to the scientific revolution. Harari rolls his eyes at this story. Nothing guaranteed that printing would be used for science, he notes

Copernicus’s On the Revolutions of the Heavenly Spheres failed to sell its puny initial print run of about 500 copies in 1543. It was, the writer Arthur Koestler joked, an “all-time worst seller.”

The book that did sell was Heinrich Kramer’s The Hammer of the Witches (1486), which ranted about a supposed satanic conspiracy of sexually voracious women who copulated with demons and cursed men’s penises. The historian Tamar Herzig describes Kramer’s treatise as “arguably the most misogynistic text to appear in print in premodern times.” Yet it was “a bestseller by early modern standards,”

Kramer’s book encouraged the witch hunts that killed tens of thousands. These murderous sprees, Harari observes, were “made worse” by the printing press.

Ampler information flows made surveillance and tyranny worse too, Harari argues. The Soviet Union was, among other things, “one of the most formidable information networks in history,”

Information has always carried this destructive potential, Harari believes. Yet up until now, he argues, even such hellish episodes have been only that: episodes

Demagogic manias like the ones Kramer fueled tend to burn bright and flame out.

States ruled by top-down terror have a durability problem too, Harari explains. Even if they could somehow intercept every letter and plant informants in every household, they’d still need to intelligently analyze all of the incoming reports. No regime has come close to managing this

for the 20th-century states that got nearest to total control, persistent problems managing information made basic governance difficult.

So it was, at any rate, in the age of paper. Collecting data is now much, much easier.

Some people worry that the government will implant a chip in their brain, but they should “instead worry about the smartphones on which they read these conspiracy theories,” Harari writes. Phones can already track our eye movements, record our speech, and deliver our private communications to nameless strangers. They are listening devices that, astonishingly, people are willing to leave by the bedside while having sex.

Harari’s biggest worry is what happens when AI enters the chat. Currently, massive data collection is offset, as it has always been, by the difficulties of data analysis

What defense could there be against an entity that recognized every face, knew every mood, and weaponized that information?

Today’s political deliriums are stoked by click-maximizing algorithms that steer people toward “engaging” content, which is often whatever feeds their righteous rage.

Imagine what will happen, Harari writes, when bots generate that content themselves, personalizing and continually adjusting it to flood the dopamine receptors of each user.

Kramer’s Hammer of the Witches will seem like a mild sugar high compared with the heroin rush of content the algorithms will concoct. If AI seizes command, it could make serfs or psychopaths of us all.

Harari regards AI as ultimately unfathomable—and that is his concern.

Although we know how to make AI models, we don’t understand them. We’ve blithely summoned an “alien intelligence,” Harari writes, with no idea what it will do.

Last year, Harari signed an open letter warning of the “profound risks to society and humanity” posed by unleashing “powerful digital minds that no one—not even their creators—can understand, predict, or reliably control.” It called for a pause of at least six months on training advanced AI systems,

cynics saw the letter as self-serving. It fed the hype by insisting that artificial intelligence, rather than being a buggy product with limited use, was an epochal development. It showcased tech leaders’ Oppenheimer-style moral seriousness

it cost them nothing, as there was no chance their research would actually stop. Four months after signing, Musk publicly launched an AI company.

The economics of the Information Age have been treacherous. They’ve made content cheaper to consume but less profitable to produce. Consider the effect of the free-content and targeted-advertising models on journalism

Since 2005, the United States has lost nearly a third of its newspapers and more than two-thirds of its newspaper jobs, to the point where nearly 7 percent of newspaper employees now work for a single organization, The New York Times

we speak of “news deserts,” places where reporting has essentially vanished.

AI threatens to exacerbate this. With better chatbots, platforms won’t need to link to external content, because they’ll reproduce it synthetically. Instead of a Google search that sends users to outside sites, a chatbot query will summarize those sites, keeping users within Google’s walled garden.

a Truman Show–style bubble: personally generated content, read by voices that sound real but aren’t, plus product placement

this would cut off writers and publishers—the ones actually generating ideas—from readers. Our intellectual institutions would wither, and the internet would devolve into a closed loop of “five giant websites, each filled with screenshots of the other four,” as the software engineer Tom Eastman puts it.

Harari is Silicon Valley’s ideal of what a chatbot should be. He raids libraries, detects the patterns, and boils all of history down to bullet points. (Modernity, he writes, “can be summarised in a single phrase: humans agree to give up meaning in exchange for power.”)

Individual AI models cost billions of dollars. In 2023, about a fifth of venture capital in North America and Europe went to AI. Such sums make sense only if tech firms can earn enormous revenues off their product, by monopolizing it or marketing it. And at that scale, the most obvious buyers are other large companies or governments. How confident are we that giving more power to corporations and states will turn out well?

He discusses it as something that simply happened. Its arrival is nobody’s fault in particular.

In Harari’s view, “power always stems from cooperation between large numbers of humans”; it is the product of society.

like a chatbot, he has a quasi-antagonistic relationship with his sources, an I’ll read them so you don’t have to attitude. He mines other writers for material—a neat quip, a telling anecdote—but rarely seems taken with anyone else’s view

Hand-wringing about the possibility that AI developers will lose control of their creation, like the sorcerer’s apprentice, distracts from the more plausible scenario that they won’t lose control, and that they’ll use or sell it as planned. A better German fable might be Richard Wagner’s The Ring of the Nibelung : A power-hungry incel forges a ring that will let its owner rule the world—and the gods wage war over it.

Harari’s eyes are more on the horizon than on Silicon Valley’s economics or politics.

In Nexus, he proposes four principles. The first is “benevolence,” explained thus: “When a computer network collects information on me, that information should be used to help me rather than manipulate me.”

Harari’s other three values are decentralization of informational channels, accountability from those who collect our data, and some respite from algorithmic surveillance

these are fine, but they are quick, unsurprising, and—especially when expressed in the abstract, as things that “we” should all strive for—not very helpful.

though his persistent first-person pluralizing (“decisions we all make”) softly suggests that AI is humanity’s collective creation rather than the product of certain corporations and the individuals who run them. This obscures the most important actors in the drama—ironically, just as those actors are sapping our intellectual life, hampering the robust, informed debates we’d need in order to make the decisions Harari envisions.

Taking AI seriously might mean directly confronting the companies developing it

Harari slots easily into the dominant worldview of Silicon Valley. Despite his oft-noted digital abstemiousness, he exemplifies its style of gathering and presenting information. And, like many in that world, he combines technological dystopianism with political passivity.

Although he thinks tech giants, in further developing AI, might end humankind, he does not treat thwarting them as an urgent priority. His epic narratives, told as stories of humanity as a whole, do not make much room for such us-versus-them clashes.

a lot of people who came out of third-world countries felt, “We are never going to catch up with the West if we just wait for the market to work its way over hundreds of years.” They looked at, in the ’30s, the Soviet Union and thought, “This is a way to accelerate modernization, industrialization.” They all were much more comfortable with the idea of something that sped up the historical process of modernization.

Milton Friedman used to say that there are two groups of people who don’t like the free market. Academics, intellectuals because they think they can do it better than the market, and businessmen because they don’t like competition. What they really want — this is a variation of the Peter Thiel argument — what they all really want is to be monopolists. That former part is, I think, what explains the Bengali intellectuals.

I think that the reality is, the market is much more powerful than they are in these areas. To give you one simple example, they decided, “Okay, we need to be making high-end chips.” Who do they bet on? They bet on Intel, a company that has failed miserably to compete with TSMC, the great Taiwanese chip manufacturer. Intel is now getting multi-billion-dollar grants from the United States government, from the European Union, because it fills all the categories that you’re looking for: big company, stable and well-run, in some sense, can guarantee a lot of jobs.

But of course, the reality is that chip making is so complicated

Who knew that, actually, it’s Nvidia, whose chips turned out to be designed for gaming, turned out to be ideal for artificial intelligence? That’s a perfect example of how the Hayekian market signals that come bottom-up are much more powerful than a political elite who tries to tell you what it is.

COWEN: What did you learn from the Anglican Book of Common Prayer?

One was a reverence for tradition, and in particular, I loved the hymnal. I think Britain’s great contribution to music is religious music. It doesn’t have anything to compete with the Germans and the Italians in opera and things like that. Religious music, I think the Brits and the English have done particularly well.

The second thing I would say is an admiration for Christianity for its extraordinary emphasis on being nice to people who have not been lucky in life. I would say that’s, to me, the central message of Christianity that I take, certainly from the Sermon on the Mount, and it’s imbued through the Book of Common Prayer: to be nice to the people who have been less fortunate than you. Be nice to poor people. Recognize that in God’s kingdom, the first shall be last and the last shall be first.

There is an enormous emphasis on the idea that those things that make you powerful in this world are not the things that really matter, that your dignity as a human being doesn’t come from that. I think that’s a very powerful idea. It’s a very revolutionary idea

Tom Holland has a very good book about this. He’s a wonderful historian in Britain. I think it’s called Dominion.

He points out what a revolutionary idea this was. It completely upended the Roman values, which were very much, the first shall be first. The powerful and the rich are the ones to be valued. He points out, here is this Jewish preacher coming out of the Middle East saying, “No, the first shall be last, the last shall be first in the kingdom of heaven.”

COWEN: I went to Amritsar the year before, and it was one of the most magical feelings I’ve ever had in any place. I’m still not sure what exactly I can trace it to — I am not a Sikh, of course. But what, for you, accounts for the strong, powerful, wondrous feeling one gets from that place?

I think there’s something about it architecturally, which is that there is a serenity about it. Sometimes you can find Hindu temples that are very elaborate. Sikhism is a kind of offshoot of Hinduism. The Hindu temples can be very elaborate, but very elaborate and ornate. This somehow has a simplicity to it. When you add to that the water — I’ve always thought that water adds an enormously calming effect

Hindi and Urdu are two Indian languages, very related. They both have roughly the same grammatical structure, but then Hindi derives its vocabulary entirely from Sanskrit, or almost entirely from Sanskrit, and Urdu derives its vocabulary almost entirely from Persian. Urdu is a language of Indian Muslims and is the official language for Pakistan. It’s a beautiful language, very lyrical, very much influenced by that Persian literary sensibility.

If you’re speaking one of the languages, there’s a way to alternate between both, which a lot of Indian politicians used to do as a way of signaling a broad embrace of both the Hindu and the Muslim communities. Nehru, India’s first prime minister, used to often do that. He would say, “I am delighted to be coming here to your home.” He’d repeat the word home, first in Urdu, then in Hindi, so that in effect, both constituencies were covered.

Modi, by contrast, India’s current prime minister, is a great Hindu nationalist. He takes pains almost never to use an Urdu word when he speaks. He speaks in a kind of highly Sanskritized Hindi that most Indians actually find hard to understand because the everyday language, Bollywood Hindi, is a mixture of Hindi words and Urdu words

I think the partition of India was a complete travesty. It was premised on this notion of religious nationalism. It was horrendously executed. The person who drew the lines, a man named Radcliffe, had never been to India. He’d never been east of the Suez and was given this task, and he did it in a month or two, probably caused a million-and-a-half to two million lives lost, maybe 10 million people displaced. It broke that wonderfully diverse, syncretic aspect of India.

If you look at cities like Delhi and Lahore, what was beautiful about them is that they mix together all the influences of India: Hindu, Muslim, Punjabi, Sindhi. Now what you have is much more bifurcated. If you go to Lahore, Lahore is a Muslim city in Pakistan, and it has a Punjabi influence. Delhi has become, essentially, much more Indian and Hindu and has lost that Muslim influence. To me, as somebody who really loves cosmopolitanism and diversity, it’s sad to see that. It’s almost like you’ve lost something that really made these places wonderfully rich.

I feel the same way when you read about the history of Europe. You think of a place like Vienna, which, in its most dazzling moment, was dazzling precisely because it was this polyglot population of people coming from all over the Habsburg Empire. A large segment of it was Jewish, and it had, as a result — think about Freud and Klimt and the music that came out of there, and the architecture that came out at the turn of the 19th century. And it’s all gone. It’s like, at this point, a somewhat beautiful but slightly dull Austrian city.

I remember once being asked when I was a graduate student at Harvard — Tony Lake was then national security adviser, and his office called and said — I’d written something in the New York Times, I think — “Mr. Lake would like you to come to the White House to brief him.”

I think, in a sense, Islam fit in within that tapestry very easily, and it’s been around for a while. When people talk about cleansing India, Hindu nationals talk about cleansing India of foreign influences. Islam has been in India since the 11th century, so it’s been around for a long time

I was amazed that America — it wasn’t America; it was where I was at Yale and Harvard and all that — that nobody cared where I came from. Nobody cared.

the syncretic nature of India, that India has always been diverse. Hinduism is very tolerant. It’s a kind of unusual religion in that you can believe in one god and be Hindu. You can believe in 300. You can be vegetarian and believe that’s a religious dictate. You can be nonvegetarian and believe that that’s completely compatible with your religion. It’s always embraced almost every variant and variation.

I walked in and there were five people around the table: Tony Lake; Deputy National Security Advisor Sandy Berger; George Stephanopoulos, who was then director of communications at the White House; Joe Nye, who was a senior professor at Harvard; one other person; and myself. And I kept thinking to myself, “Are they going to realize at some point that I’m not an American citizen? They’re asking me for my advice on what America should do, and I am on a student visa.” And of course, nobody ever did, which is one of the great glories of America.

My thesis topic was, I tried to answer the question, when countries rise in great power, when they rise economically, they become great powers because they quickly translate that economic power into diplomatic and military power. What explains the principal exception in modern history, which is the United States?

My simple answer was that the United States was a very unusual creature in the modern world. It was a very strong nation with a very weak state. The federal government in the United States did not have the capacity to extract the resources from the society at large because you didn’t have income taxes in those days.

COWEN: What put you off academia? And this was for the better, in my view.

ZAKARIA: I think two things. One, I could see that political science was moving away from the political science that I loved, which was a broad discipline rooted in the social sciences but also rooted in the humanities, which was rigorous, structural, historical comparisons. Looking at different countries, trying to understand why there were differences.

It was moving much more toward a huge emphasis on things like rational choice, on game theory There was an economist envy. Just as economists have math envy, political scientists have economist envy. It was moving in that direction

COWEN: After 9/11 in 2001, you wrote a famous essay for Newsweek, “Why Do They Hate Us?” You talked about the rulers, failed ideas, religion. If you were to revise or rethink that piece today, how would you change it? Because we have 23 more years of data, right?

He had a routine, which is, he’d get up about 6:00 a.m. He’d go down to the basement of his townhouse, and at 6:30, he would start writing or working on whatever his next big research project was. He’d do that, uninterrupted, for three hours at least, sometimes four. Then, at about 9:30, 10:00, he would take the subway to Harvard.

His point was, you got to start the day by doing the important work of academia, which is producing knowledge. All the rest of it — teaching, committee meetings, all that — you can do later. He was so disciplined about that, that every five years or so, he put out another major piece of work, another major book

I looked at that, and I said to myself, I do not have the self-discipline to perform at that level. I need to go into something that has deadlines,

It’s all within you, and you have to be able to generate ideas from that lonely space. I’ve always found that hard. For me, writing books is the hardest thing I do. I feel like I have to do it because I feel as though everything else is trivia — the television, column, everything else.

The second piece of it was actually very much related to Huntington. Sam Huntington was quite an extraordinary character, probably the most important social scientist in the second half of the 20th century. Huge contributions to several fields of political science. He lived next to me

ZAKARIA: Yes. Not very much, honestly. The central point I was making in that essay was that if you look at the Arab world, it is the principal outlier in the modern era, where it has undergone almost no political modernization.

The Arab world had remained absolutely static. My argument was that it was largely because of the curse of oil and oil wealth, which had impeded modernization. But along with that, because of that failed modernization, they had developed this reactionary ideology of Islam, which said the answer is to go further back, not to go forward. “Islam is the solution,” was the cry of the Islamic fundamentalists in the 1970s.

COWEN: I’m struck that this year, both you and Ruchir Sharma have books coming out — again, Fareed’s book is Age of Revolutions: Progress and Backlash from 1600 to the Present — that I would describe broadly as classically liberal. Do you think classical liberalism is making a comeback

the reason these books are coming out — and certainly, mine, as you know, is centrally occupied with the problem that there’s a great danger that we are going to lose this enormous, probably the most important thing that’s happened in the last 500, 600 years in human history, this movement that has allowed for the creation of modern liberal democratic societies with somewhat market economies.

If you look at the graph of income, of GDP, per capita GDP, it’s like a straight line. There’s no improvement until you get to about, roughly speaking, the 17th, 18th century in Europe, and then you see a sharp uptick. You see this extraordinary rise, and that coincides with the rise of science and intellectual curiosity and the scientific method, and the industrial revolution after that. All that was a product of this great burst of liberal Enlightenment thinking in the West.

If you think about what we’ve gone through in the last 30 years — and this is really the central argument in my book — massive expansion of globalization, massive expansion of information technology so that it has completely upended the old economy. All of this happening, and people are overwhelmed, and they search in that age of anxiety. They search for a solution, and the easy solutions are the ones offered by the populists.

They’re deeply anti-liberal, illiberal. So, I worry that, actually, if we don’t cherish what we have, we’ll lose what has been one of the great, great periods of progress in human history.

COWEN: Why does your book cover the 17th-century Dutch Golden Age? ZAKARIA: The Dutch are the first modern country. If you think about politics before that — certainly with the exception of ancient Greece and Rome — in modern history, the Dutch invent modern politics and economics. They invent modern politics in the sense that it’s the first time politics is not about courts and kings. It is about a merchant republic with powerful factions and interest groups and political parties, or the precursor to political parties.

It’s the beginning of modern economics because it’s economics based not simply on land and agriculture, but on the famous thing that John Locke talked about, which is mixing human beings’ labor with the land. The Dutch literally do this when they reclaim land from the sea and find ways to manage it, and then invent tall ships, which is, in some ways, one of the first great technological revolutions that has a direct economic impact.

You put all that together, and the Dutch — they become the richest country in the world, and they become the leading technological power in the world. It was very important to me to start the story — because they are really the beginnings of modern liberalism

COWEN: Circa 1800, how large were the Chinese and Indian economies?

Circa 1800, the Chinese and Indian economies are the two largest economies in the world, and people have taken this to mean, oh, the West had a temporary spurt because of colonies and cheap energy, and that the Chinese and Indians are just coming back to where they were.

First of all, the statistic is misleading because in those days, GDP was simply measured by using population. All society was agricultural. The more people you had, the larger your GDP. It was meaningless because the state could not extract that GDP in any meaningful way, and it’s meaningless because it doesn’t measure progress. It doesn’t measure per capita GDP growth, which is the most important thing to look at.

If you look at per capita GDP growth from 1350 to 1950, for 600 years, India and China have basically no movement. It’s about $600 in 1350 and $600 in 1950. The West, by comparison, moves up 600 percent in that period. It’s roughly $500 per capita GDP to roughly $5,000 per capita GDP.

You can also look at all kinds of other measures. You can look at diet. There are economic historians who’ve done this very well, and people in England were eating four to five times as much grain and protein as people in China and India. You can look at the extraordinary flourishing of science and engineering. You can look at the rise of the great universities. It’s all happening in the West.

The reason this is important is, people need to understand the rise of the West has been a very profound, deep-rooted historical phenomenon that began sometime in the 15th century. The fact that we’re moving out of that phase is a big, big deal. This is not a momentary blip. This is a huge train. The West define modernity. Even when countries try to be modern, they are in some way becoming Western because there is no path we know of to modernity without that.

One other way of just thinking about how silly that statistic is: in pure GDP terms, China had a larger GDP than Britain in 1900. Now, look at Britain in 1900: the most advanced industrial society in the world, ruling one-quarter of the world, largest navy in the world, was able to humiliate China by using a small fraction of its military power during the opium era. That’s what tells you that number is really meaningless. The West has been significantly more advanced than the rest of the world since the 16th century at least.

The documents also raise new questions about Facebook, which is already grappling with intense criticism over the spread of Russian propaganda and fake news. The data Cambridge collected from profiles, a portion of which was viewed by The Times, included details on users’ identities, friend networks and “likes.”

“Protecting people’s information is at the heart of everything we do,” Mr. Grewal said. “No systems were infiltrated, and no passwords or sensitive pieces of information were stolen or hacked.”Still, he added, “it’s a serious abuse of our rules.”

The group experimented abroad, including in the Caribbean and Africa, where privacy rules were lax or nonexistent and politicians employing SCL were happy to provide government-held data, former employees said.

Mr. Nix and his colleagues courted Mr. Mercer, who believed a sophisticated data company could make him a kingmaker in Republican politics, and his daughter Rebekah, who shared his conservative views. Mr. Bannon was intrigued by the possibility of using personality profiling to shift America’s culture and rewire its politics, recalled Mr. Wylie and other former employees, who spoke on the condition of anonymity because they had signed nondisclosure agreements.

Mr. Wylie’s team had a bigger problem. Building psychographic profiles on a national scale required data the company could not gather without huge expense. Traditional analytics firms used voting records and consumer purchase histories to try to predict political beliefs and voting behavior.

But those kinds of records were useless for figuring out whether a particular voter was, say, a neurotic introvert, a religious extrovert, a fair-minded liberal or a fan of the occult. Those were among the psychological traits the firm claimed would provide a uniquely powerful means of designing political messages.

Mr. Wylie found a solution at Cambridge University’s Psychometrics Centre. Researchers there had developed a technique to map personality traits based on what people had liked on Facebook. The researchers paid users small sums to take a personality quiz and download an app, which would scrape some private information from the their profiles and those of their friends, activity that Facebook permitted at the time. The approach, the scientists said, could reveal more about a person than their parents or romantic partners knew — a claim that has been disputed.

When the Psychometrics Centre declined to work with the firm, Mr. Wylie found someone who would: Dr. Kogan, who was then a psychology professor at the university and knew of the techniques. Dr. Kogan built his own app and in June 2014 began harvesting data for Cambridge Analytica. The business covered the costs — more than $800,000 — and allowed him to keep a copy for his own research, according to company emails and financial records.

He ultimately provided over 50 million raw profiles to the firm, Mr. Wylie said, a number confirmed by a company email and a former colleague. Of those, roughly 30 million contained enough information, including places of residence, that the company could match users to other records and build psychographic profiles. Only about 270,000 users — those who participated in the survey — had consented to having their data harvested.Image

Under the guidance of Brad Parscale, Mr. Trump’s digital director in 2016 and now the campaign manager for his 2020 re-election effort, Cambridge performed a variety of services, former campaign officials said. That included designing target audiences for digital ads and fund-raising appeals, modeling voter turnout, buying $5 million in television ads and determining where Mr. Trump should travel to best drum up support.

“We wanted as much as we could get,” he acknowledged. “Where it came from, who said we could have it — we weren’t really asking.”

The firm was effectively a shell. According to the documents and former employees, any contracts won by Cambridge, originally incorporated in Delaware, would be serviced by London-based SCL and overseen by Mr. Nix, a British citizen who held dual appointments at Cambridge Analytica and SCL. Most SCL employees and contractors were Canadian, like Mr. Wylie, or European.

In a memo to Mr. Bannon, Ms. Mercer and Mr. Nix, the lawyer, then at the firm Bracewell & Giuliani, warned that Mr. Nix would have to recuse himself “from substantive management” of any clients involved in United States elections. The data firm would also have to find American citizens or green card holders, Mr. Levy wrote, “to manage the work and decision making functions, relative to campaign messaging and expenditures.”

In summer and fall 2014, Cambridge Analytica dived into the American midterm elections, mobilizing SCL contractors and employees around the country. Few Americans were involved in the work, which included polling, focus groups and message development for the John Bolton Super PAC, conservative groups in Colorado and the campaign of Senator Thom Tillis, the North Carolina Republican.

While Cambridge hired more Americans to work on the races that year, most of its data scientists were citizens of the United Kingdom or other European countries, according to two former employees.

Mr. Wylie said the Facebook data was “the saving grace” that let his team deliver the models it had promised the Mercers.

Mr. Grewal, the Facebook deputy general counsel, said in a statement that both Dr. Kogan and “SCL Group and Cambridge Analytica certified to us that they destroyed the data in question.”

But copies of the data still remain beyond Facebook’s control. The Times viewed a set of raw data from the profiles Cambridge Analytica obtained.

While Mr. Nix has told lawmakers that the company does not have Facebook data, a former employee said that he had recently seen hundreds of gigabytes on Cambridge servers, and that the files were not encrypted.

Today, as Cambridge Analytica seeks to expand its business in the United States and overseas, Mr. Nix has mentioned some questionable practices. This January, in undercover footage filmed by Channel 4 News in Britain and viewed by The Times, he boasted of employing front companies and former spies on behalf of political clients around the world, and even suggested ways to entrap politicians in compromising situations.

Mr. Nix is seeking to take psychographics to the commercial advertising market. He has repositioned himself as a guru for the digital ad age — a “Math Man,” he puts it. In the United States last year, a former employee said, Cambridge pitched Mercedes-Benz, MetLife and the brewer AB InBev, but has not signed them on.

Mr. Wylie found a solution at Cambridge University’s Psychometrics Centre. Researchers there had developed a technique to map personality traits based on what people had liked on Facebook. The researchers paid users small sums to take a personality quiz and download an app, which would scrape some private information from their profiles and those of their friends, activity that Facebook permitted at the time. The approach, the scientists said, could reveal more about a person than their parents or romantic partners knew

If all of this sounds familiar, it’s because we basically saw this same environmental dynamic play out several years ago with tech’s last big obsession: Crypto and web3. In that case, Bitcoin emerged as the industry’s obvious environmental sore spot due to the vast amounts of energy needed to mine coins in its proof of work model. Some estimates suggest Bitocin alone requires more energy every year than Norway’s annual electricity consumption.

rs of criticism from environmental activists however led the crypto industry to make some changes. Ethereum, the second largest currency on the blockchain, officially switched last year to a proof of stake model which supporters claim could reduce its power usage by over 99%. Other smaller coins similarly were designed with energy efficiency in mind. In the grand scheme of things, large language models are still in their infancy and it’s far from certain how its environmental report card will play out.

Opponents of Mr. Obama’s climate policy called the court’s action historic.“We are thrilled that the Supreme Court realized the rule’s immediate impact and froze its implementation, protecting workers and saving countless dollars as our fight against its legality continues,” said Patrick Morrisey, the attorney general of West Virginia, which has led the 29-state legal challenge.

The challenged regulation, which was issued last summer by the Environmental Protection Agency, requires states to make major cuts to greenhouse gas pollution created by electric power plants, the nation’s largest source of such emissions. The plan could transform the nation’s electricity system, cutting emissions from existing power plants by a third by 2030, from a 2005 baseline, by closing hundreds of heavily polluting coal-fired plants and increasing production of wind and solar power. Continue reading the main story

“Climate change is the most significant environmental challenge of our day, and it is already affecting national public health, welfare and the environment,” Solicitor General Donald B. Verrilli Jr. wrote in a brief urging the Supreme Court to reject a request for a stay while the case moves forward.

The regulation calls for states to submit compliance plans by September, though they may seek a two-year extension. The first deadline for power plants to reduce their emissions is in 2022, with full compliance not required until 2030.The states challenging the regulation, led mostly by Republicans and many with economies that rely on coal mining or coal-fired power, sued to stop what they called “the most far-reaching and burdensome rule the E.P.A. has ever forced onto the states.”

The states urged the Supreme Court to take immediate action to block what they called a “power grab” under which “the federal environmental regulator seeks to reorganize the energy grids in nearly every state in the nation.” Though the first emission reduction obligations do not take effect until 2022, the states said they had already started to spend money and shift resources.

Not only have pro-Palestinian rallies been stopped or broken up by police

ermany’s unwavering official support for the Israeli government’s actions leaves scant room for humanity. It is also counter-productive, serving to spread fear, Islamophobia and, yes, antisemitism

In addition to banning Hamas-related symbols schools are now free to also ban “symbols, gestures and expressions of opinion that do not yet reach the limit of criminal liability”, which includes the keffiyeh, the Palestinian flag and “free Palestine” stickers and badges.

The stifling of opposition to the killing of civilians in Gaza even extends to Jewish people. A Jewish Israeli woman who held up a placard in a Berlin square calling for an end to violence was approached by German police in a matter of seconds and taken away in a police van. She was later released. Anyone showing solidarity with Palestinians is automatically suspected of being a tacit Hamas sympathiser.

German education senator Katharina Günther-Wünsch has also sent out an email to schools saying that “any demonstrative behaviour or expression of opinion that can be understood (italics are mine) as approving the attacks against Israel or supporting the terrorist organisations that carry them out, such as Hamas or Hezbollah, represents a threat to school peace in the current situation and is prohibited

Having grown up in the shadow of collective guilt for Nazi war crimes, many German intellectuals seem almost to welcome an opportunity to atone for ancestral sins. The atonement, of course, will fall on the backs of Palestinian children.

It should come under the “stating the obvious” category, but still has to be underlined: historically, Islamophobia has only led to more terrorism. Having grown up in Bosnia, I can tell you with absolute certainty that the vicious circle is never-ending. There is always another dead body to be weaponised.

Living in Germany, I see it as my human responsibility to call it out for its one-sidedness, its hypocrisy and its acquiesence in the ethnic cleansing of Gaza. Walking by Lucie’s stone every day, I am reminded of that responsibility. I am reminded of what silence can do and how long it can haunt a place and a people. I come from a silent place soaked in blood. I never thought I would feel that same silence in Germany.

I am appalled by Hamas’s actions and offer my thoughts for their victims, but I have no say in what they do. None of my taxes go to the funding of Hamas. Some of my taxes, however, do fund the bombing of Gaza. In the period between 2018 and 2022, Israel imported $2.7bn-worth of weapons from the US and Germany.

Either you are against fascism in all its forms, or you’re a hypocrite. You condemn a terrorist organisation as well as the terrorism committed by a government

Problems of success are harder, not easier, to deal with, of course – you wouldn’t want to reverse the conditions that got us here

The path of incentives is easy to trace: an addicted customer is a reliable customer – and why settle for mere consumption of your product when you could get overconsumption instead?

David Courtwright calls this “limbic capitalism”: named for the part of the brain responsible for emotional processing. Global industries, he says, are starting to aim right at it.

In this country, we are at last regulating away some of the older vices, such as nicotine and alcohol.

many new vices are popping up in their stead. Food companies mine and refine their products for addictive properties: ultra-processed food, thought to encourage compulsive eating, now makes up two-thirds of the calorie intake of adolescent Britons

Gaming addiction is soaring. We hear less about workaholics than we used to, but perhaps only because the condition is so common – instead, we hear about burnout, the end result.

Then of course there are smartphones, teaching us to crave for the next ping of a message, or the bright notification of a retweet

These in turn link us to the thousands of addictive products being pumped out by the largest tech companies on the planet. There are gambling apps, gaming apps and one-click shopping apps – even addiction to fitness-tracker apps is on the rise. Then of course there is social media, to which almost half of British teens now feel addicted.

For a few, it is possible to buy your way out, back into old-fashioned reality

a new kind of luxury is emerging: freedom from cravings.

The ultimate example might be the rapid growth of semaglutide drugs

they also seem to reduce cravings for alcohol, nicotine and opioids, and perhaps even compulsive gambling and online shopping.

semaglutides such as Wegovy and Ozempic reduce the release of dopamine in the brain’s striatum, the region that motivates you to take another bite of something delicious but also to take another puff of your cigarette. They appear to act not on the digestive system, but on craving itself.

It’s hard to imagine – having a smartphone without needing to check it, putting down a tube of Pringles halfway through. For this is the major struggle of modern life: self-control in the face of addictive products. It’s becoming harder and harder to do

most cannot afford to anyway

Ozempic is mostly known as a “Hollywood phenomenon”, available only to the rich.

The income divide is there, too, when it comes to resisting online addictions. As time away from screens becomes a scarce commodity, some companies are monetising it in the form of off-grid digital detox weekends or curiously expensive dumbphones.

This summer, Eton announced it would ban smartphones – giving new pupils Nokia “bricks” instead. Meanwhile, children from lower-income backgrounds spend on average two more hours a day on their phones than their peers.

money shelters you from many of the conditions that encourage addiction in the first place. Junk food appeals most when you do not have the time, money or the emotional energy to access healthy alternatives: chasing their likely customers, fast food joints spring up in deprived areas. As do betting shops.

We sometimes pretend that resisting gambling, social media, sweet treats and retail therapy is mostly a matter of willpower – as if the economy weren’t built on pushing us these things. Instead, it’s becoming a privilege few can afford.

“He disavowed us,” Mr. Johnson acknowledged, “but he explained why there is so much anger in America that I couldn’t have asked for a better approach from him.”

Like others in his world, Mr. Taylor does not know if Mr. Trump agrees with him on everything. In an interview, he suggested that it did not really matter, and that Mr. Trump was expressing the discomfort many white people felt about other races.

Asked six weeks later about Mr. Duke’s support, he said he had been unaware of it: “David Duke endorsed me? O.K. All right. I disavow, O.K.?” Later, on Twitter, he repeated the phrase: “I disavow.”

But on the flatlands of social media, the border between Mr. Trump and white supremacists easily blurs. He has retweeted supportive messages from racist or nationalist Twitter accounts to his nine million followers. Last fall, he retweeted a graphic with fictitious crime statistics claiming that 81 percent of white homicide victims in 2015 were killed by blacks. (No such statistic was available for 2015 at the time; the actual figure for 2014 was 15 percent, according to the F.B.I.)

In January and February he retweeted messages from a user with the handle @WhiteGenocideTM, whose profile picture is of George Lincoln Rockwell, the founder of the American Nazi Party. A couple of days later, in quick succession, he retweeted two more accounts featuring white nationalist or Nazi themes. Mr. Trump deleted one of the retweets, but white supremacists saw more than a twitch of the thumb. “Our Glorious Leader and ULTIMATE SAVIOR has gone full wink-wink-wink to his most aggressive supporters,” Mr. Anglin wrote on The Daily Stormer.

In fact, Mr. Trump’s Twitter presence is tightly interwoven with hordes of mostly anonymous accounts trafficking in racist and anti-Semitic attacks. When Little Bird, a social media data mining company, analyzed a week of Mr. Trump’s Twitter activity, it found that almost 30 percent of the accounts Mr. Trump retweeted in turn followed one or more of 50 popular self-identified white nationalist accounts.

After Mr. Trump attacked a profile of his wife, Melania, in GQ, the article’s author, the journalist Julia Ioffe, who is Jewish, was inundated with anti-Semitic abuse on social media, including a cartoon depicting Ms. Ioffe in a concentration camp.Asked whether he condemned the attacks, Mr. Trump told an interviewer: “I don’t have a message to the fans. A woman wrote an article that’s inaccurate.

Last month, President Biden’s trade representative, Katherine Tai, notified the World Trade Organization that the American government no longer supported a proposal it once spearheaded that would have exported the American laissez-faire approach to tech. Had that proposal been adopted, it would have spared tech companies the headache of having to deal with many different domestic laws about how data must be handled, including rules mandating that it be stored or analyzed locally. It also would have largely shielded tech companies from regulations aimed at protecting citizens’ privacy and curbing monopolistic behavior.

The move to drop support for that digital trade agenda has been pilloried as disaster for American companies and a boon to China, which has a host of complicated restrictions on transferring data outside of China. “We have warned for years that either the United States would write the rules for digital trade or China would,” Senator Mike Crapo, a Republican from Idaho, lamented in a press statement. “Now, the Biden administration has decided to give China the pen.”

While some of this agenda is reasonable and good for the world — too much regulation stifles innovation — adopting this agenda wholesale would risk cementing the advantages that big American tech companies already enjoy and permanently distorting the market in their favor.

who used to answer the phone and interact with lobbyists at the U.S. trade representative’s office. The paper includes redacted emails between Trump-era trade negotiators and lobbyists for Facebook, Google, Microsoft and Amazon, exchanging suggestions for the proposed text for the policy on digital trade in the United States-Mexico-Canada Agreement. “While they were previously ‘allergic to Washington,’ as one trade negotiator described, over the course of a decade, technology companies hired lobbyists and joined trade associations with the goal of proactively influencing international trade policy,” Ms. Li wrote in the Socio-Economic Review.

That paper explains how U.S. trade officials came to champion a digital trade policy agenda that was nearly identical to what Google, Apple and Meta wanted: No restrictions on the flow of data across borders. No forced disclosure of source codes or algorithms in the normal course of business. No laws that would curb monopolies or encourage more competition — a position that is often cloaked in clauses prohibiting discrimination against American companies. (Since so many of the monopolistic big tech players are American, rules targeting such behavior disproportionately fall on American companies, and can be portrayed as unfair barriers to trade.)

This approach essentially takes the power to regulate data out of the hands of governments and gives it to technology companies, according to research by Henry Gao, a Singapore-based expert on international trade.

The truth is that Ms. Tai is taking the pen away from Meta, Google and Amazon, which helped shape the previous policy, according to a research paper published this year by Wendy Li,

Many smaller tech companies complain that big players engage in monopolistic behavior that should be regulated. For instance, Google has been accused of privileging its own products in search results, while Apple has been accused of charging some developers exorbitant fees to be listed in its App Store. A group of smaller tech companies called the Coalition for App Fairness thanked Ms. Tai for dropping support for the so-called tech-friendly agenda at the World Trade Organization.

Still, Ms. Tai’s reversal stunned American allies and foreign business leaders and upended negotiations over digital trade rules in the Indo-Pacific Economic Framework, one of Mr. Biden’s signature initiatives in Asia.

The about-face was certainly abrupt: Japan, Singapore and Australia — which supported the previous U.S. position — were left on their own. It’s unfortunate that U.S. allies and even some American officials were taken by surprise. But changing stances was the right call.

The previous American position at the World Trade Organization was a minority position. Only 34 percent of countries in the world have open data transfer policies like the United States, according to a 2021 World Bank working paper, while 57 percent have adopted policies like the European Union’s, which allow data to flow freely but leave room for laws that protect privacy and personal data.

Nine percent of countries have restrictive data transfer policies, including Russia and China.

The United States now has an opportunity to hammer out a sensible global consensus that gives tech companies what they need — clarity, more universal rules, and relative freedom to move data across borders — without shielding them from the kinds of regulations that might be required to protect society and competition in the future.

If the Biden administration can shepherd a digital agreement that strikes the right balance, there’s a chance that it will also restore faith in free trade by showing that trade agreements don’t have to be written by the powerful at the expense of the weak.

It has recruited talent from Microsoft, Google, Amazon.com and Myspace and is using a powerful, multiplatform approach to predicting consumer behavior that could raise its standing among investors and clients.

Acxiom has its own classification system, PersonicX, which assigns consumers to one of 70 detailed socioeconomic clusters and markets to them accordingly. In this situation, it pegs Mr. Hughes as a “savvy single” — meaning he’s in a cluster of mobile, upper-middle-class people who do their banking online, attend pro sports events, are sensitive to prices — and respond to free-shipping offers.