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The electors cast their ballots for president and vice president via paper ballot. Thirty-three states and the District of Columbia legally require their electors to choose whoever won the state’s popular vote, so there should be no surprises there. The other 17 states don’t “bind” their electors, meaning they can vote for whomever they choose.

After the electors cast their ballots, the votes are counted and the electors sign certificates showing the results. These are paired with certificates from the governor’s office showing the state’s vote totals.

Congress officially counts the votes in a joint session held in the House chamber on Jan. 6, with Mr. Pence presiding. Mr. Pence opens the certificates

The session cannot be ended until the count is complete and the result publicly declared. At this point, the election is officially decided. The only remaining task is the inauguration on Jan. 20.

Democrats will hold control of the House. And Republicans will control the Senate, regardless of the results of the Georgia runoff elections on Jan. 5, because Mr. Pence will still be in office to act as the tiebreaking vote if the chamber is split 50 to 50.

Any objection to a state’s results must be made in writing and be signed by at least one senator and one member of the House. The two chambers would then separate to debate the objection.

Stopping Mr. Biden from assuming office remains a long-shot strategy for Republicans.For an objection to stand, it must pass both houses of Congress by a simple majority. If the vote followed party lines, Republicans could not block Mr. Biden’s victory.

With some Trump allies already planning objections, the congressional session is likely to make for good political theater. But the process has little chance of changing the outcome of the election.

The Supreme Court on Friday rejected a desperate 11th-hour effort by Trump allies to change the outcome of the election, the latest in a string of stinging legal defeats.

From protests outside the voting sites to livestreamed broadcasts of the activities inside the rooms, electors, state officials and party leaders are bracing for an extraordinary onslaught of attention.

Even as the electors prepared to vote on Monday, Mr. Trump on Sunday railed on Twitter against the “MOST CORRUPT ELECTION IN U.S. HISTORY” and suggested that swing states could not certify “without committing a severely punishable crime” — further raising concerns about electors’ personal security.

Even some Republicans who are more willing to acknowledge electoral reality seem unable to completely give up hope.

A broader effort to persuade Republican-controlled state legislatures to swap out Democratic electors for a slate loyal to Mr. Trump has also failed.

For Democrats, the Electoral College vote will be the final affirmation of defeat for a president they believe has undermined the foundation of the country’s political system.

Enshrined in the Constitution, electors are called into action weeks after an election is over.

As a result, more than half of the states plan to livestream their events, to provide transparency and pre-empt some of the conspiratorial thinking that many state officials anticipate will follow their events.

After the electors cast their ballots, the votes are counted and the electors sign certificates showing the results.

Still, none of that, he said, overshadowed how “exhilarating and humbling” it is to be one of the 16 Democratic electors, the first in Georgia in nearly three decades, the last time a Democrat won the state.

A Wisconsin elector, State Representative Shelia Stubbs of Madison, said she cried with joy after being named an elector this year.

Those students, he says, "ought to be benefited. And by the way, a majority of those students are African American, and that's an issue that can't be ignored in this discussion either."

Popular sports bring in money for other athletic programs But at the same time, Roberts notes the NCAA system has benefited students who have scholarships to play other sports

The case before the Supreme Court involves 126 teams that play big-time football and men's and women's basketball. But for all of college sports, Roberts and other sports law experts say, the lower courts have left these issues in a legal mess.

California, for instance, passed a law last year effectively requiring schools to allow athletes to profit from their names, images, and likenesses. After the law was enacted, the NCAA abruptly reversed its long-held opposition to such benefits, and said it would issue new policies early next year.

its long-held opposition to such benefits, and said it would issue new policies early nex

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A Supreme Court decision siding with the NCAA would likely fortify the NCAA's effort to maintain tighter restrictions on benefits for big-time college and basketball players. A decision holding that the NCAA has gone too far would likely lead to more benefits for players whose hard work and frequent injuries allow the schools they play for to reap billions in TV and other revenue.

While that revenue sometimes benefits lesser-known sports and players at those schools, many experts say it more often benefits coaches and assistant coaches who are paid tens of millions of dollars, and allows schools to spend millions on mammoth stadiums and lavish locker rooms.

Last week 60 minutes reported that "at least 30 universities have cut almost 100 programs: soccer, squash, golf, gymnastics. Football powerhouse Clemson cut men's track and field. Stanford eliminated 11 sports. Schools are honoring existing scholarships, but more than 1,500 student-athletes, both men and women, will no longer have a team to compete for."

Zig-zagging through this legal minefield will be difficult for the Supreme Court, to say the least. And it may well issue a narrow opinion that still leaves most of these questions unresolved. But the fact that the justices decided to take on the this case, when it has dodged similar ones in the past, indicates they are at least serious about the issues.

In other words, AOC can win in NY-14 but AOC-type candidates are no solution at all to the Democrats’ working class problems in most areas of the country. The grim fact is that as education polarization has played out geographically, Democratic votes are now distributed so inefficiently that the translation of these votes into political power has been severely undercut

As a result, Democrats’ hold on political power is ever on a knife’s edge even when their last election was a good one (as it is today).

Consistent with other recent research, the study finds that:

Working-class voters prefer progressive candidates who focus primarily on bread-and-butter economic issues, and who frame those issues in universal terms.

Candidates whose campaigns focused primarily on universalist policy issues such as jobs, health care, and the economy performed better than those who focused on group-specific policies, such as racial justice or immigration.

In addition, woke messaging decreased the appeal of other candidate characteristics. For example, candidates employing woke messaging who championed either centrist or progressive economic, health care, or civil rights policy priorities were viewed less favorably than their counterparts who championed the same priorities but opted for universalist messaging.

easier said than done given Democrats’ recent evolution away from universalist messaging and toward foregrounding the equity effects of unambiguously universal programs and deferring to group specific concerns around race, gender and sexuality, even where they are clearly unpopular and dubious as policy. The result has a been a shift in the Democratic party “brand” that has created barriers to Democratic party voting among broad swathes of the working class.

The grim prospects for the Democrats in 2022 and the gut-wrenching prospect of losing the White House to Donald Trump (again) in 2024 make clear the stakes.

A Democratic brand reset is clearly in order to stop the bleeding among working class voters, along the lines suggested by the Jacobin study. A good way to start would be to embrace widely-held American views and values that are particularly strong among the multiracial working class.

Equality of opportunity is a fundamental American principle; equality of outcome is not.America is not perfect but it is good to be patriotic and proud of the country.

Discrimination and racism are bad but they are not the cause of all disparities in American society.No one is completely without bias but calling all white people racists who benefit from white privilege and American society a white supremacist society is not right or fair.

America benefits from the presence of immigrants and no immigrant, even if illegal, should be mistreated. But border security is still important, as is an enforceable system that fairly decides who can enter the country.Police misconduct and brutality against people of any race is wrong and we need to reform police conduct and recruitment. But crime is a real problem so more and better policing is needed for public safety. That cannot be provided by “defunding the police”.

There are underlying differences between men and women but discrimination on the basis of gender is wrong.There are basically two genders but people who want to live as a gender different from their biological sex should have that right and not be discriminated against. However, there are issues around child consent to transitioning and participation in women’s sports that are complicated and not settled.

Racial achievement gaps are bad and we should seek to close them. However, they are not due just to racism and standards of high achievement should be maintained for people of all races.Language policing has gone too far; by and large, people should be able to express their views without fear of sanction by employer, school, institution or government. Good faith should be assumed, not bad faith.

Besides positively embracing these views it is necessary for major Democratic officeholders and candidates to actively dissociate themselves and their party from the woke stances that contradict these views and tarnish their brand among working class voters. That entails not just saying that one does not endorse now-familiar strands of cultural leftism but in some well-chosen places directly criticizing by name some who hold extreme views that are associated with the Democrats. That will be of great assistance in getting the message through to average working class voters.

these views are entirely consistent with a very progressive Democratic program in the areas of health care, education, social programs, jobs and the economy that would disproportionately benefit the poor and working class, and therefore blacks and Hispanics.

In that sense, one might respond to the inevitable accusation that a universalist, mainstream approach is tantamount to throwing loyal Democratic constituencies in need of help “under the bus”: who is throwing whom under the bus? Perhaps it is those who stand in the way of a Democratic approach that could plausibly generate the widest possible support that are throwing those who need help the most under the bus.

And this approach, unlike the Democrats’ current default setting, has the potential to make the Democrats and their progressive policies consistent winners

Meanwhile, donors such as Tesla CEO Elon Musk, disgraced FTX founder Sam Bankman-Fried, Skype founder Jaan Tallinn and ethereum co-founder Vitalik Buterin — as well as institutions like Open Philanthropy, a charitable organization started by billionaire Facebook co-founder Dustin Moskovitz — have worked to push doomsayers from the tech industry’s margins into the mainstream.

More recently, wealthy tech philanthropists have begun recruiting an army of elite college students to prioritize the fight against rogue AI over other threats

Other skeptics, like venture capitalist Marc Andreessen, are AI boosters who say that hyping such fears will impede the technology’s progress.

Critics call the AI safety movement unscientific. They say its claims about existential risk can sound closer to a religion than research

And while the sci-fi narrative resonates with public fears about runaway AI, critics say it obsesses over one kind of catastrophe to the exclusion of many others.

Open Philanthropy spokesperson Mike Levine said harms like algorithmic racism deserve a robust response. But he said those problems stem from the same root issue: AI systems not behaving as their programmers intended. The theoretical risks “were not garnering sufficient attention from others — in part because these issues were perceived as speculative,” Levine said in a statement. He compared the nonprofit’s AI focus to its work on pandemics, which also was regarded as theoretical until the coronavirus emerged.

Among the reputational hazards of the AI safety movement is its association with an array of controversial figures and ideas, like EA, which is also known for recruiting ambitious young people on elite college campuses.

The foundation began prioritizing existential risks around AI in 2016,

there was little status or money to be gained by focusing on risks. So the nonprofit set out to build a pipeline of young people who would filter into top companies and agitate for change from the insid

Colleges have been key to this growth strategy, serving as both a pathway to prestige and a recruiting ground for idealistic talent

The clubs train students in machine learning and help them find jobs in AI start-ups or one of the many nonprofit groups dedicated to AI safety.

Many of these newly minted student leaders view rogue AI as an urgent and neglected threat, potentially rivaling climate change in its ability to end human life. Many see advanced AI as the Manhattan Project of their generation

Despite the school’s ties to Silicon Valley, Mukobi said it lags behind nearby UC Berkeley, where younger faculty members research AI alignment, the term for embedding human ethics into AI systems.

Mukobi joined Stanford’s club for effective altruism, known as EA, a philosophical movement that advocates doing maximum good by calculating the expected value of charitable acts, like protecting the future from runaway AI. By 2022, AI capabilities were advancing all around him — wild developments that made those warnings seem prescient.

At Stanford, Open Philanthropy awarded Luby and Edwards more than $1.5 million in grants to launch the Stanford Existential Risk Initiative, which supports student research in the growing field known as “AI safety” or “AI alignment.

from the start EA was intertwined with tech subcultures interested in futurism and rationalist thought. Over time, global poverty slid down the cause list, while rogue AI climbed toward the top.

In the past year, EA has been beset by scandal, including the fall of Bankman-Fried, one of its largest donors

Another key figure, Oxford philosopher Nick Bostrom, whose 2014 bestseller “Superintelligence” is essential reading in EA circles, met public uproar when a decades-old diatribe about IQ surfaced in January.

Programming future AI systems to share human values could mean “an amazing world free from diseases, poverty, and suffering,” while failure could unleash “human extinction or our permanent disempowerment,” Mukobi wrote, offering free boba tea to anyone who attended the 30-minute intro.

Open Philanthropy’s new university fellowship offers a hefty direct deposit: undergraduate leaders receive as much as $80,000 a year, plus $14,500 for health insurance, and up to $100,000 a year to cover group expenses.

Student leaders have access to a glut of resources from donor-sponsored organizations, including an “AI Safety Fundamentals” curriculum developed by an OpenAI employee.

Interest in the topic is also growing among Stanford faculty members, Edwards said. He noted that a new postdoctoral fellow will lead a class on alignment next semester in Stanford’s storied computer science department.

Edwards discovered that shared online forums function like a form of peer review, with authors changing their original text in response to the comments

Mukobi feels energized about the growing consensus that these risks are worth exploring. He heard students talking about AI safety in the halls of Gates, the computer science building, in May after Geoffrey Hinton, another “godfather” of AI, quit Google to warn about AI. By the end of the year, Mukobi thinks the subject could be a dinner-table topic, just like climate change or the war in Ukraine.

Luby, Edwards’s teaching partner for the class on human extinction, also seems to find these arguments persuasive. He had already rearranged the order of his AI lesson plans to help students see the imminent risks from AI. No one needs to “drink the EA Kool-Aid” to have genuine concerns, he said.

Edwards, on the other hand, still sees things like climate change as a bigger threat than rogue AI. But ChatGPT and the rapid release of AI models has convinced him that there should be room to think about AI safety.

Interested students join reading groups where they get free copies of books like “The Precipice,” and may spend hours reading the latest alignment papers, posting career advice on the Effective Altruism forum, or adjusting their P(doom), a subjective estimate of the probability that advanced AI will end badly. The grants, travel, leadership roles for inexperienced graduates and sponsored co-working spaces build a close-knit community.

The course will not be taught by students or outside experts. Instead, he said, it “will be a regular Stanford class.”

In the late 1990s, the sociologists Kathryn Edin and Maria Kefalas interviewed 162 low-income single mothers in Camden, N.J., and Philadelphia to understand why they had children without being married. “Money is seldom the primary reason” why mothers say they are no longer with their children’s fathers. Instead, mothers point to “far more serious” offenses: “It is the drug and alcohol abuse, the criminal behavior and consequent incarceration, the repeated infidelity, and the patterns of intimate violence that are the villains looming largest in poor mothers’ accounts of relational failure.”

But it doesn’t take behavior this harmful to discourage marriage; often, simple compatibility or constancy can be elusive.

Ms. Camino, for her part, has dabbled in dating since her partner left, but hasn’t yet met anyone who shares her values, someone who’s funny and — she hesitates to use the word “feminist” — but a man who won’t just roll his eyes and say something about being on her period whenever she voices an opinion.

“There are women that are just out here trying, and the men aren’t ready,” she told me. “They don’t care, most of them.” Who, exactly, is Ms. Camino supposed to marry?

For as long as people have been promoting marriage, they have also been observing that a good man is hard to find

what was once dismissed as the complaint of “picky” women is now supported by a raft of data

The same pundits plugging marriage also bemoan the crisis among men and boys, what has come to be known as “male drift” — men turning away from college, dropping out of the work force, or failing to look after their health.

even this nod ignores the qualitative aspect of the dating experience — the part that’s hard to cover in surveys, or address with policy.

a senior fellow at the American Enterprise Institute who recently surveyed more than 5,000 Americans about dating and relationships, found that nearly half of college-educated women said they were single because they had trouble finding someone who meets their expectations, versus one-third of men.

For a variety of reasons — mixed messages from the broader culture about toughness and vulnerability, the activity-oriented nature of male friendships — it seems that by the time men begin dating, they are relatively “limited in their ability and willingness to be fully emotionally present and available,” he said.

Navigating interpersonal relationships in a time of evolving gender norms and expectations “requires a level of emotional sensitivity that I think some men probably just lack, or they don’t have the experience,

He had recently read about a high school creative writing assignment in which boys and girls were asked to imagine a day from the perspective of the opposite sex. While girls wrote detailed essays showing they had already spent significant time thinking about the subject, many boys simply refused to do the exercise, or did so resentfully

Mr. Cox likened that to heterosexual relationships today: “The girls do extra and the boys do little or nothing.”

educated women freeze their eggs because they’re unable to find a suitable male partner: Ms. Inhorn points to a large gap in the number of college-educated women versus college-educated men during their reproductive years — on the order of several million.

Ms. Inhorn’s book goes beyond these quantitative mismatches to document the qualitative experience of women who are actively searching for partners — the frustration, hurt and disappointment.

“Almost without exception,” she writes, “women in this study were ‘trying hard’ to find a loving partner,” mostly through dating sites and apps. Women in their late 30s reported “online ageism,” others described removing their Ph.D from their profiles so as not to intimidate potential dates; still others found that men were often commitment-averse.

Ms. Inhorn compiled a sort of taxonomy of cads, such as the “Alpha males” who “want to be challenged by work, not by their partners” or the “Polyamorous men” who claim “that their multiple attachments to women are all ‘committed.’” Her breakdown — table 1.1 in the book — reads like a rigorous academic version of all the complaints you’ve ever heard from your single female friends.

to truly address the decline in heterosexual marriage, we must attend to the details — to acknowledge the qualitative aspects of relationship formation

in particular, we should listen to the experiences of women who are attempting to find partners. We should care about the interior lives, not just the educational attainment or the employment status, of the men who could be those partners.

It requires taking the stories of single women seriously, and not treating them as punchlines — something for which there is little historical precedent, but which a handful of scholars are slowly beginning to do.

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.

“Write a five-paragraph essay on Virginia Woolf’s ‘To the Lighthouse.’” Too generic? Well, how about “Write a five-paragraph essay on the theme of loss in ‘To the Lighthouse’”? Too high-schoolish? “Add some bigger words, please.” The product might not be ready to turn in the moment it is born, fully formed, from ChatGPT’s head. But with enough tweaking — either by the student or by the machine at the student’s demand — chances are the output can muster at least a passing grade.

Which of these uses are okay? Which aren’t? The harnessing of an AI tool to create an annotated bibliography likely doesn’t rankle even librarians the way relying on that same tool to draft a reflection on Virginia Woolf offends the professor of the modern novel. Why? Because that kind of contemplation goes closer to the heart of what education is really about.

the core of the question colleges now face. They can’t really stop students from using AI in class. They might not be able to notice students have done so at all, and when they do think they’ve noticed they’ll be acting only on suspicion. But maybe teachers can control the ways in which students use AI in class.

Figuring out exactly what ways those ought to be requires educators to determine what they care about in essays — what they are desperate to hear. The purpose of these papers is for students to demonstrate what they’ve learned, from hard facts to compositional know-how, and for teachers to assess how their pupils are progressing. The answer to what teachers want to get from students in their written work depends on what they want to give to students.

ChatGPT is sort of in a class of its own, because it can be almost anything its users want it to be so long as they possess one essential skill: prompt engineering. This means, basically, manipulating the machine not only into giving you an answer but also into giving you the kind of answer you’re looking for.

The next concern is that students should use AI in a manner that improves not only their writing but also their thinking — in short, in a manner that enhances learning rather than bypasses the need to learn at all.

This simple principle makes for complicated practice. Certainly, no one is going to learn anything by letting AI write an essay in its entirety. What about letting AI brainstorm an idea, on the other hand, or write an outline, or gin up a counter-argument? Lyndsey Cook, a senior at Ole Miss planning a career in nursing, finds the brainstorming especially helpful: She’ll ask ChatGPT or another tool to identify the themes in a piece of literature, and then she’ll go back and look for them herself.

These shortcuts, on the one hand, might interfere with students’ learning to brainstorm, outline or see the other side of things on their own

But — here comes a human-generated counterargument — they may also aid students in surmounting obstacles in their composition that otherwise would have stopped them short. That’s particularly true of kids whose high schools didn’t send them to college already equipped with these capabilities.

Allow AI to boost you over these early hurdles, and suddenly the opportunity for deeper learning — the opportunity to really write — will open up. That’s how Caleb Jackson, the part-time student for whom Perplexity has been such a boon, sees it: His professor, he says , wanted them to “get away from the high-school paper and go further, to write something larger like a thesis.”

maybe, as one young Ole Miss faculty member put it to me, this risks “losing the value of the struggle.” That, she says, is what she is scared will go away.

All this invites the most important question there is: What is learning for?

Learning, in college, can be instrumental. According to this view, the aim of teaching is to prepare students to live in the real world, so all that really matters is whether they have the chops to field jobs that feed themselves and their families. Perhaps knowing how to use AI to do any given task for you, then, is one of the most valuable skills out there — the same way it pays to be quick with a calculator.

If you accept this line of argument, however, there are still drawbacks to robotic crutches. Some level of critical thinking is necessary to function as an adult, and if AI stymies its development even the instrumental aim of education is thwarted. The same goes for that “value of the struggle.” The real world is full of adversity, much of which the largest language model can’t tell you how to overcome.

more compelling is the idea, probably shared by most college professors, that learning isn’t only instrumental after all — that it has intrinsic value and that it is the end rather than merely a means to one.

Every step along the way that is skipped, the shorter the journey becomes, the less we will take in as we travel.

This glummest of outlooks suggests that AI will stunt personal growth even if it doesn’t harm professional prospects.

While that doesn’t mean it’s wise to prohibit every little application of the technology in class, it probably does mean discouraging those most closely related to critical thinking.

One approach is to alter standards for grading, so that the things the machines are worst at are also the things that earn the best marks: originality, say, or depth of feeling, or so-called metacognition — the process of thinking about one’s own thinking or one’s own learning.

Hopefully, these things are also the most valuable because they are what make us human.

Caleb Jackson only wants AI to help him write his papers — not to write them for him. “If ChatGPT will get you an A, and you yourself might get a C, it’s like, ‘Well, I earned that C.’” He pauses. “That might sound crazy.”

Dominic Tovar agrees. Let AI take charge of everything, and, “They’re not so much tools at that point. They’re just replacing you.”

Lyndsey Cook, too, believes that even if these systems could reliably find the answers to the most vexing research problems, “it would take away from research itself” — because scientific inquiry is valuable for its own sake. “To have AI say, ‘Hey, this is the answer …’” she trails off, sounding dispirited.

Claire Mischker, lecturer of composition and director of the Ole Miss graduate writing center, asked her students at the end of last semester to turn in short reflections on their experience in her class. She received submissions that she was near certain were produced by ChatGPT — “that,” she says as sarcastically as she does mournfully, “felt really good.

The central theme of the course was empathy.

All the empathy and humility in the world will not mean much against the pressures of intolerance and tribalism without an invaluable quality that Sulzberger did not emphasise: courage.

Sulzberger seems to underestimate the struggle he is in, that all journalism and indeed America itself is in

In describing the essential qualities of independent journalism in his essay, he unspooled a list of admirable traits – empathy, humility, curiosity and so forth. These qualities have for generations been helpful in contending with the Times’s familiar problem, which is liberal bias

on their own, these qualities have no chance against the Times’s new, more dangerous problem, which is in crucial respects the opposite of the old one.

The Times’s problem has metastasised from liberal bias to illiberal bias, from an inclination to favour one side of the national debate to an impulse to shut debate down altogether

the internet knocked the industry off its foundations. Local newspapers were the proving ground between college campuses and national newsrooms. As they disintegrated, the national news media lost a source of seasoned reporters and many Americans lost a journalism whose truth they could verify with their own eyes.

far more than when I set out to become a journalist, doing the work right today demands a particular kind of courage:

the moral and intellectual courage to take the other side seriously and to report truths and ideas that your own side demonises for fear they will harm its cause.

One of the glories of embracing illiberalism is that, like Trump, you are always right about everything, and so you are justified in shouting disagreement down.

leaders of many workplaces and boardrooms across America find that it is so much easier to compromise than to confront – to give a little ground today in the belief you can ultimately bring people around

This is how reasonable Republican leaders lost control of their party to Trump and how liberal-minded college presidents lost control of their campuses. And it is why the leadership of the New York Times is losing control of its principles.

Over the decades the Times and other mainstream news organisations failed plenty of times to live up to their commitments to integrity and open-mindedness. The relentless struggle against biases and preconceptions, rather than the achievement of a superhuman objective omniscience, is what mattered

. I thought, and still think, that no American institution could have a better chance than the Times, by virtue of its principles, its history, its people and its hold on the attention of influential Americans, to lead the resistance to the corruption of political and intellectual life, to overcome the encroaching dogmatism and intolerance.

As the country became more polarised, the national media followed the money by serving partisan audiences the versions of reality they preferred

This relationship proved self-reinforcing. As Americans became freer to choose among alternative versions of reality, their polarisation intensified.

as the top editors let bias creep into certain areas of coverage, such as culture, lifestyle and business, that made the core harder to defend and undermined the authority of even the best reporters.

here have been signs the Times is trying to recover the courage of its convictions

The paper was slow to display much curiosity about the hard question of the proper medical protocols for trans children; but once it did, the editors defended their coverage against the inevitable criticism.

As Sulzberger told me in the past, returning to the old standards will require agonising change. He saw that as the gradual work of many years, but I think he is mistaken. To overcome the cultural and commercial pressures the Times faces, particularly given the severe test posed by another Trump candidacy and possible presidency, its publisher and senior editors will have to be bolder than that.

As a Democrat from a family of Democrats, a graduate of Yale and a blossom of the imagined meritocracy, I had my first real chance, at Buchanan’s rallies, to see the world through the eyes of stalwart opponents of abortion, immigration and the relentlessly rising tide of modernity.

the Times is failing to face up to one crucial reason: that it has lost faith in Americans, too.

For now, to assert that the Times plays by the same rules it always has is to commit a hypocrisy that is transparent to conservatives, dangerous to liberals and bad for the country as a whole.

It makes the Times too easy for conservatives to dismiss and too easy for progressives to believe.

The reality is that the Times is becoming the publication through which America’s progressive elite talks to itself about an America that does not really exist.

It is hard to imagine a path back to saner American politics that does not traverse a common ground of shared fact.

It is equally hard to imagine how America’s diversity can continue to be a source of strength, rather than become a fatal flaw, if Americans are afraid or unwilling to listen to each other.

I suppose it is also pretty grandiose to think you might help fix all that. But that hope, to me, is what makes journalism worth doing.

Since Adolph Ochs bought the paper in 1896, one of the most inspiring things the Times has said about itself is that it does its work “without fear or favour”. That is not true of the institution today – it cannot be, not when its journalists are afraid to trust readers with a mainstream conservative argument such as Cotton’s, and its leaders are afraid to say otherwise.

Most important, the Times, probably more than any other American institution, could influence the way society approached debate and engagement with opposing views. If Times Opinion demonstrated the same kind of intellectual courage and curiosity that my colleagues at the Atlantic had shown, I hoped, the rest of the media would follow.

You did not have to go along with everything that any tribe said. You did not have to pretend that the good guys, much as you might have respected them, were right about everything, or that the bad guys, much as you might have disdained them, never had a point. You did not, in other words, ever have to lie.

This fundamental honesty was vital for readers, because it equipped them to make better, more informed judgments about the world. Sometimes it might shock or upset them by failing to conform to their picture of reality. But it also granted them the respect of acknowledging that they were able to work things out for themselves.

The Atlantic did not aspire to the same role as the Times. It did not promise to serve up the news of the day without any bias. But it was to opinion journalism what the Times’s reporting was supposed to be to news: honest and open to the world.

Those were the glory days of the blog, and we hit on the idea of creating a living op-ed page, a collective of bloggers with different points of view but a shared intellectual honesty who would argue out the meaning of the news of the day

They were brilliant, gutsy writers, and their disagreements were deep enough that I used to joke that my main work as editor was to prevent fistfights.

Under its owner, David Bradley, my colleagues and I distilled our purpose as publishing big arguments about big ideas

we also began producing some of the most important work in American journalism: Nicholas Carr on whether Google was “making us stupid”; Hanna Rosin on “the end of men”; Taylor Branch on “the shame of college sports”; Ta-Nehisi Coates on “the case for reparations”; Greg Lukianoff and Jonathan Haidt on “the coddling of the American mind”.

I was starting to see some effects of the new campus politics within the Atlantic. A promising new editor had created a digital form for aspiring freelancers to fill out, and she wanted to ask them to disclose their racial and sexual identity. Why? Because, she said, if we were to write about the trans community, for example, we would ask a trans person to write the story

There was a good argument for that, I acknowledged, and it sometimes might be the right answer. But as I thought about the old people, auto workers and abortion opponents I had learned from, I told her there was also an argument for correspondents who brought an outsider’s ignorance, along with curiosity and empathy, to the story.

A journalism that starts out assuming it knows the answers, it seemed to me then, and seems even more so to me now, can be far less valuable to the reader than a journalism that starts out with a humbling awareness that it knows nothing.

In the age of the internet it is hard even for a child to sustain an “innocent eye”, but the alternative for journalists remains as dangerous as ever, to become propagandists. America has more than enough of those already.

When I looked around the Opinion department, change was not what I perceived. Excellent writers and editors were doing excellent work. But the department’s journalism was consumed with politics and foreign affairs in an era when readers were also fascinated by changes in technology, business, science and culture.

Fairly quickly, though, I realised two things: first, that if I did my job as I thought it should be done, and as the Sulzbergers said they wanted me to do it, I would be too polarising internally ever to lead the newsroom; second, that I did not want that job, though no one but my wife believed me when I said that.

there was a compensating moral and psychological privilege that came with aspiring to journalistic neutrality and open-mindedness, despised as they might understandably be by partisans. Unlike the duelling politicians and advocates of all kinds, unlike the corporate chieftains and their critics, unlike even the sainted non-profit workers, you did not have to pretend things were simpler than they actually were

On the right and left, America’s elites now talk within their tribes, and get angry or contemptuous on those occasions when they happen to overhear the other conclave. If they could be coaxed to agree what they were arguing about, and the rules by which they would argue about it, opinion journalism could serve a foundational need of the democracy by fostering diverse and inclusive debate. Who could be against that?

The large staff of op-ed editors contained only a couple of women. Although the 11 columnists were individually admirable, only two of them were women and only one was a person of colour

Not only did they all focus on politics and foreign affairs, but during the 2016 campaign, no columnist shared, in broad terms, the worldview of the ascendant progressives of the Democratic Party, incarnated by Bernie Sanders. And only two were conservative.

This last fact was of particular concern to the elder Sulzberger. He told me the Times needed more conservative voices, and that its own editorial line had become predictably left-wing. “Too many liberals,” read my notes about the Opinion line-up from a meeting I had with him and Mark Thompson, then the chief executive, as I was preparing to rejoin the paper. “Even conservatives are liberals’ idea of a conservative.” The last note I took from that meeting was: “Can’t ignore 150m conservative Americans.”

As I knew from my time at the Atlantic, this kind of structural transformation can be frightening and even infuriating for those understandably proud of things as they are. It is hard on everyone

experience at the Atlantic also taught me that pursuing new ways of doing journalism in pursuit of venerable institutional principles created enthusiasm for change. I expected that same dynamic to allay concerns at the Times.

If Opinion published a wider range of views, it would help frame a set of shared arguments that corresponded to, and drew upon, the set of shared facts coming from the newsroom.

New progressive voices were celebrated within the Times. But in contrast to the Wall Street Journal and the Washington Post, conservative voices – even eloquent anti-Trump conservative voices – were despised, regardless of how many leftists might surround them.

The Opinion department mocked the paper’s claim to value diversity. It did not have a single black editor

Eventually, it sank in that my snotty joke was actually on me: I was the one ignorantly fighting a battle that was already lost. The old liberal embrace of inclusive debate that reflected the country’s breadth of views had given way to a new intolerance for the opinions of roughly half of American voters.

Out of naivety or arrogance, I was slow to recognise that at the Times, unlike at the Atlantic, these values were no longer universally accepted, let alone esteemed

After the 9/11 attacks, as the bureau chief in Jerusalem, I spent a lot of time in the Gaza Strip interviewing Hamas leaders, recruiters and foot soldiers, trying to understand and describe their murderous ideology. Some readers complained that I was providing a platform for terrorists, but there was never any objection from within the Times.

Our role, we knew, was to help readers understand such threats, and this required empathetic – not sympathetic – reporting. This is not an easy distinction but good reporters make it: they learn to understand and communicate the sources and nature of a toxic ideology without justifying it, much less advocating it.

Today’s newsroom turns that moral logic on its head, at least when it comes to fellow Americans. Unlike the views of Hamas, the views of many Americans have come to seem dangerous to engage in the absence of explicit condemnation

Focusing on potential perpetrators – “platforming” them by explaining rather than judging their views – is believed to empower them to do more harm.

After the profile of the Ohio man was published, media Twitter lit up with attacks on the article as “normalising” Nazism and white nationalism, and the Times convulsed internally. The Times wound up publishing a cringing editor’s note that hung the writer out to dry and approvingly quoted some of the criticism, including a tweet from a Washington Post opinion editor asking, “Instead of long, glowing profiles of Nazis/White nationalists, why don’t we profile the victims of their ideologies”?

the Times lacked the confidence to defend its own work

The editor’s note paraded the principle of publishing such pieces, saying it was important to “shed more light, not less, on the most extreme corners of American life”. But less light is what the readers got. As a reporter in the newsroom, you’d have to have been an idiot after that explosion to attempt such a profile

Empathetic reporting about Trump supporters became even more rare. It became a cliché among influential left-wing columnists and editors that blinkered political reporters interviewed a few Trump supporters in diners and came away suckered into thinking there was something besides racism that could explain anyone’s support for the man.

After a year spent publishing editorials attacking Trump and his policies, I thought it would be a demonstration of Timesian open-mindedness to give his supporters their say. Also, I thought the letters were interesting, so I turned over the entire editorial page to the Trump letters.

I wasn’t surprised that we got some criticism on Twitter. But I was astonished by the fury of my Times colleagues. I found myself facing an angry internal town hall, trying to justify what to me was an obvious journalistic decision

Didn’t he think other Times readers should understand the sources of Trump’s support? Didn’t he also see it was a wonderful thing that some Trump supporters did not just dismiss the Times as fake news, but still believed in it enough to respond thoughtfully to an invitation to share their views?

And if the Times could not bear to publish the views of Americans who supported Trump, why should it be surprised that those voters would not trust it?

Two years later, in 2020, Baquet acknowledged that in 2016 the Times had failed to take seriously the idea that Trump could become president partly because it failed to send its reporters out into America to listen to voters and understand “the turmoil in the country”. And, he continued, the Times still did not understand the views of many Americans

Speaking four months before we published the Cotton op-ed, he said that to argue that the views of such voters should not appear in the Times was “not journalistic”.

Conservative arguments in the Opinion pages reliably started uproars within the Times. Sometimes I would hear directly from colleagues who had the grace to confront me with their concerns; more often they would take to the company’s Slack channels or Twitter to advertise their distress in front of each other

This environment of enforced group-think, inside and outside the paper, was hard even on liberal opinion writers. One left-of-centre columnist told me that he was reluctant to appear in the New York office for fear of being accosted by colleagues.

An internal survey shortly after I left the paper found that barely half the staff, within an enterprise ostensibly devoted to telling the truth, agreed “there is a free exchange of views in this company” and “people are not afraid to say what they really think”.)

Even columnists with impeccable leftist bona fides recoiled from tackling subjects when their point of view might depart from progressive orthodoxy.

The bias had become so pervasive, even in the senior editing ranks of the newsroom, as to be unconscious

Trying to be helpful, one of the top newsroom editors urged me to start attaching trigger warnings to pieces by conservatives. It had not occurred to him how this would stigmatise certain colleagues, or what it would say to the world about the Times’s own bias

By their nature, information bubbles are powerfully self-reinforcing, and I think many Times staff have little idea how closed their world has become, or how far they are from fulfilling their compact with readers to show the world “without fear or favour”

sometimes the bias was explicit: one newsroom editor told me that, because I was publishing more conservatives, he felt he needed to push his own department further to the left.

The Times’s failure to honour its own stated principles of openness to a range of views was particularly hard on the handful of conservative writers, some of whom would complain about being flyspecked and abused by colleagues. One day when I relayed a conservative’s concern about double standards to Sulzberger, he lost his patience. He told me to inform the complaining conservative that that’s just how it was: there was a double standard and he should get used to it.

A publication that promises its readers to stand apart from politics should not have different standards for different writers based on their politics. But I delivered the message. There are many things I regret about my tenure as editorial-page editor. That is the only act of which I am ashamed.

I began to think of myself not as a benighted veteran on a remote island, but as Rip Van Winkle. I had left one newspaper, had a pleasant dream for ten years, and returned to a place I barely recognised.

The new New York Times was the product of two shocks – sudden collapse, and then sudden success. The paper almost went bankrupt during the financial crisis, and the ensuing panic provoked a crisis of confidence among its leaders. Digital competitors like the HuffPost were gaining readers and winning plaudits within the media industry as innovative. They were the cool kids; Times folk were ink-stained wrinklies.

In its panic, the Times bought out experienced reporters and editors and began hiring journalists from publications like the HuffPost who were considered “digital natives” because they had never worked in print. This hiring quickly became easier, since most digital publications financed by venture capital turned out to be bad businesses

Though they might have lacked deep or varied reporting backgrounds, some of the Times’s new hires brought skills in video and audio; others were practised at marketing themselves – building their brands, as journalists now put it – in social media. Some were brilliant and fiercely honest, in keeping with the old aspirations of the paper.

critically, the Times abandoned its practice of acculturation, including those months-long assignments on Metro covering cops and crime or housing. Many new hires who never spent time in the streets went straight into senior writing and editing roles.

All these recruits arrived with their own notions of the purpose of the Times. To me, publishing conservatives helped fulfil the paper’s mission; to them, I think, it betrayed that mission.

then, to the shock and horror of the newsroom, Trump won the presidency. In his article for Columbia Journalism Review, Sulzberger cites the Times’s failure to take Trump’s chances seriously as an example of how “prematurely shutting down inquiry and debate” can allow “conventional wisdom to ossify in a way that blinds society.

Many Times staff members – scared, angry – assumed the Times was supposed to help lead the resistance. Anxious for growth, the Times’s marketing team implicitly endorsed that idea, too.

As the number of subscribers ballooned, the marketing department tracked their expectations, and came to a nuanced conclusion. More than 95% of Times subscribers described themselves as Democrats or independents, and a vast majority of them believed the Times was also liberal

A similar majority applauded that bias; it had become “a selling point”, reported one internal marketing memo. Yet at the same time, the marketers concluded, subscribers wanted to believe that the Times was independent.

As that memo argued, even if the Times was seen as politically to the left, it was critical to its brand also to be seen as broadening its readers’ horizons, and that required “a perception of independence”.

Readers could cancel their subscriptions if the Times challenged their worldview by reporting the truth without regard to politics. As a result, the Times’s long-term civic value was coming into conflict with the paper’s short-term shareholder value

The Times has every right to pursue the commercial strategy that makes it the most money. But leaning into a partisan audience creates a powerful dynamic. Nobody warned the new subscribers to the Times that it might disappoint them by reporting truths that conflicted with their expectations

When your product is “independent journalism”, that commercial strategy is tricky, because too much independence might alienate your audience, while too little can lead to charges of hypocrisy that strike at the heart of the brand.

It became one of Dean Baquet’s frequent mordant jokes that he missed the old advertising-based business model, because, compared with subscribers, advertisers felt so much less sense of ownership over the journalism

The Times was slow to break it to its readers that there was less to Trump’s ties to Russia than they were hoping, and more to Hunter Biden’s laptop, that Trump might be right that covid came from a Chinese lab, that masks were not always effective against the virus, that shutting down schools for many months was a bad idea.

there has been a sea change over the past ten years in how journalists think about pursuing justice. The reporters’ creed used to have its foundation in liberalism, in the classic philosophical sense. The exercise of a reporter’s curiosity and empathy, given scope by the constitutional protections of free speech, would equip readers with the best information to form their own judgments. The best ideas and arguments would win out

The journalist’s role was to be a sworn witness; the readers’ role was to be judge and jury. In its idealised form, journalism was lonely, prickly, unpopular work, because it was only through unrelenting scepticism and questioning that society could advance. If everyone the reporter knew thought X, the reporter’s role was to ask: why X?

Illiberal journalists have a different philosophy, and they have their reasons for it. They are more concerned with group rights than individual rights, which they regard as a bulwark for the privileges of white men. They have seen the principle of  free speech used to protect right-wing outfits like Project Veritas and Breitbart News and are uneasy with it.

They had their suspicions of their fellow citizens’ judgment confirmed by Trump’s election, and do not believe readers can be trusted with potentially dangerous ideas or facts. They are not out to achieve social justice as the knock-on effect of pursuing truth; they want to pursue it head-on

The term “objectivity” to them is code for ignoring the poor and weak and cosying up to power, as journalists often have done.

And they do not just want to be part of the cool crowd. They need to be

To be more valued by their peers and their contacts – and hold sway over their bosses – they need a lot of followers in social media. That means they must be seen to applaud the right sentiments of the right people in social media

The journalist from central casting used to be a loner, contrarian or a misfit. Now journalism is becoming another job for joiners, or, to borrow Twitter’s own parlance, “followers”, a term that mocks the essence of a journalist’s role.

The new newsroom ideology seems idealistic, yet it has grown from cynical roots in academia: from the idea that there is no such thing as objective truth; that there is only narrative, and that therefore whoever controls the narrative – whoever gets to tell the version of the story that the public hears – has the whip hand

What matters, in other words, is not truth and ideas in themselves, but the power to determine both in the public mind.

By contrast, the old newsroom ideology seems cynical on its surface. It used to bug me that my editors at the Times assumed every word out of the mouth of any person in power was a lie.

And the pursuit of objectivity can seem reptilian, even nihilistic, in its abjuration of a fixed position in moral contests. But the basis of that old newsroom approach was idealistic: the notion that power ultimately lies in truth and ideas, and that the citizens of a pluralistic democracy, not leaders of any sort, must be trusted to judge both.

Our role in Times Opinion, I used to urge my colleagues, was not to tell people what to think, but to help them fulfil their desire to think for themselves.

It seems to me that putting the pursuit of truth, rather than of justice, at the top of a publication’s hierarchy of values also better serves not just truth but justice, too