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It’s worth noting that the original version of the Green New Deal was devised by the left-leaning British National Economic Foundation, as a means for recovery after the 2008 economic collapse

Once Labour’s full, staggeringly bold proposals were unveiled, support for the party soared

Labour climbed a full 20 points in the six weeks of the 2017 campaign, robbing the Tories of a majority in the Parliament

So it seems to me there is a massive opportunity for the left now across the Western world. Look at how popular a 70 percent top rate is … in America! The left is correct to sense a huge opportunity and they are right, I think, to be bold.

I wrote about Jeremy Corbyn’s Labour last year in these pages, and that, under his leadership, “it turned out to be difficult to propel a new movement of left radicalism without simultaneously tapping into a vein of left extremism,” and that seems to me to be precisely the challenge in the U.S. as well

the full package from the contemporary radicalized left in both the U.K. and U.S. brings with it far more troubling ideas

If they insist on calling our multicultural and multiracial democracy a manifestation of “white supremacy,” they will empower real white supremacists.

A passion for social justice curdles into attacks on free speech

Postmodern critical gender theory denies any meaningful natural differences between men and women, and casts an entire sex as inherently problematic

Concern about mass immigration is dismissed as nothing but racism and xenophobia.

so Labour, after so much promise and success, has not been able to get any sustainable polling lead over the most shambolic Tory government in memory.

If the Democrats want to fight the next election on the need for a radical rebalancing of the economy in favor of the middle and working class, for massive investment in new green technology, for higher taxes on the superrich, and for health-care security for all Americans, they can win

If they conflate those goals with extremist rhetoric about abolishing everyone’s current health insurance, and starting from scratch, as the Green New Deal advises, not so much

If they insist that men and women are indistinguishable, that girls can have penises and boys can have periods, as transgender ideology now demands, they’ll seem nuts to most fair-minded people.

Hostility to the policies of the state of Israel — a perfectly legitimate position — morphs swiftly into ugly anti-Semitic tropes

It’s not easy to find any heroes in Washington these days, so allow me to eulogize one. Walter Jones was a longtime Republican congressman from North Carolina, who died earlier this wee

Are they really capable of fucking this up once again? The answer that is emerging in the first months of the new Democratic House is: of course they can.

If they call a border wall an “immorality” and refuse to fund a way to detain and humanely house the huge surge of migrant families and children now overwhelming the southern border (up 290 percent over the same period in 2018, with a record 1,800 apprehensions on Monday of this week alone!), they will rightly be called in favor of open borders

I’m haunted all the time by the knowledge of what my lifetime will have witnessed. Humans are committing countless species to death; we are destroying the life of our oceans and skies; we are changing the planet’s ecosystem more quickly than at any time since the asteroids wiped out the dinosaurs. From the perspective of life itself, we are conducting a holocaust of the natural world. How is the knowledge of this not traumatizing?

A new report from the Institute for Public Policy Research notes, according to the BBC, that “since 1950, the number of floods across the world has increased by 15 times, extreme temperature events by 20 times, and wildfires seven-fold.”

Last week, research emerged showing that the insect biomass is declining by 2.5 percent a year, which means that we may wipe out the entire insect population within a century — and lose a quarter of it in the next ten years.

This amounts to what Jill Kieldash describes as the “actual structural and functional collapse of the natural systems which have supported life on Earth for the last 400 million years.”

I don’t know how this paradigm affects you every day, but it is for me the gutting context for everything, a growing nausea laced with guilt and shame.

In a century, we will have destroyed this Earth as we have known it — in absolutely full awareness of what we are doing. It’s the greatest crime humanity has ever committed

One answer could be that they are behaving in a classic way when a catastrophe strikes: They’re traumatized by this knowledge, and they cope with this trauma by a classic form of disassociation. In fact, we are all living through this collective trauma

Once we become aware of its true scope, depth, and accelerating pace, we then begin to view everything else through the traumatic lens of the climate crisis

How could anyone with a reasonably realistic, educated worldview not be haunted by the perpetual specter of Climate Trauma when considering fundamental life and identity choices?

I am not surprised by declining birth rates in the West. Having a child in today’s era means initiating another human being into the end of the world as we have known it

I find my own witnessing of the collapse of liberal democratic values in the West inseparable from the mass extinction of life on Earth our civilization has wrought — and the double depression this creates makes me want to escape

this collective trauma is never-ending. It’s a 9/11 all the time. Woodbury notes the similarity between our knowledge of future planetary collapse and a diagnosis of a terminal disease: “You may put it out of your mind for spells, but the grief associated with prospective loss comes at you in waves.

The challenge is to resist disassociation — which is “the human capacity to mentally escape an insufferable reality.

We are disassociating from America in our current dystopian politics. But we are also, more profoundly, disassociating ourselves from our deepest ecological reality: that we are killing what created us. And we cannot seem to stop.

we may be underestimating what the constant drumbeat of news about the accelerating sixth great extinction has been doing to us psychologically.

when it became clear that the Iraq War had been based on phony intelligence, he actually changed his mind. More than that: He took moral responsibility for his vote for the war, and rethought a great deal of his previous views. Ashamed of what he had done — and the lives lost because of the war — he went on to write 12,000 letters to family members of service members killed

he tirelessly fought to bring back war-making powers to the Senate, where they belong. He took on his own party leadership in demanding votes before military adventures

He was that very rare creature: a true Republican fiscal conservative

Of course a man of this character was a dogged defender of his own constituents, especially those in the military subjected to unfairness or injustice of any kin

This didn’t make him a liberal. It made him a conservative. And he proved that to be a conservative these days — a humane, decent, honest, principled conservative — you really have no place in the Republican Party

The appeal of this argument is its simplicity. You can tell if a place is enabling systemic racism merely by counting the people of color in it; and you can tell if a place isn’t by the same rubric. The drawback, of course, is that the world isn’t nearly as simple

On some measures, the NYT is already a mirror of NYC. Its staff is basically 50 - 50 on sex (with women a slight majority of all staff on the business side, and slight minority in editorial). And it’s 15 percent Asian on the business side, 10 percent in editorial, compared with 13.9 percent of NYC’s population. 

But its black percentage of staff — 10 percent in business, 9 percent in editorial — needs more than doubling to reflect demographics. Its Hispanic/Latino staff amount to only 8 percent in business and 5 percent in editorial, compared with 29 percent of New York City’s demographics, the worst discrepancy for any group

notice how this new goal obviously doesn’t reflect New York City’s demographics in many other ways. It draws overwhelmingly from the college educated, who account for only 37 percent of New Yorkers, leaving more than 60 percent of the city completed unreflected in the staffing.

We have no idea whether “white” people are Irish or Italian or Russian or Polish or Canadians in origin. Similarly, we do not know if “black” means African immigrants, or native black New Yorkers, or people from the Caribbean

Around 10 percent of staffers would have to be Republicans (and if the paper of record nationally were to reflect the country as a whole, and not just NYC, around 40 percent would have to be

Some 6 percent of the newsroom would also have to be Haredi or Orthodox Jews

48 percent of NYT employees would have to agree that religion is “very important” in their lives; and 33 percent would be Catholic.

Taking this proposal seriously, then, really does require explicit use of race in hiring, which is illegal, which is why the News Guild tweet and memo might end up causing some trouble if the policy is enforced.

It would also have to restrict itself to the literate, and, according to Literacy New York, 25 percent of people in Manhattan “lack basic prose literary skills” along with 37 percent in Brooklyn and 41 percent in the Bronx.

My point is that any attempt to make a specific institution entirely representative of the demographics of its location will founder on the sheer complexity of America’s demographic story and the nature of the institution itself

Journalism, for example, is not a profession sought by most people; it’s self-selecting for curious, trouble-making, querulous assholes who enjoy engaging with others and tracking down the truth (at least it used to be). There’s no reason this skillset or attitude will be spread evenly across populations

It seems, for example, that disproportionate numbers of Jews are drawn to it, from a culture of high literacy, intellectualism, and social activism. So why on earth shouldn’t they be over-represented? 

that’s true of other institutions too: are we to police Broadway to make sure that gays constitute only 4 percent of the employees? Or, say, nursing, to ensure that the sex balance is 50-50? Or a construction company for gender parity?

take publishing — an industry not far off what the New York Times does. 74 percent of its employees are women. Should there be a hiring freeze until the men catch up? 

The more you think about it, the more absurdly utopian the Kendi project turns out to be. That’s because its core assumption is that any demographic discrepancies between a profession or institution and its locale are entirely a function of oppression.

That’s how Kendi explains racial inequality in America, and specifically denies any alternative explanation.

So how is it that a white supremacist country has whites earning considerably less on average than Asian-Americans? How does Kendi explain the fact that the most successful minority group in America are Indian-Americans — with a median income nearly twice that of the national median?

Here’s a partial list of the national origins of US citizens whose median earnings are higher than that of white people in America: Indian, Chinese, Japanese, Pakistani, Iranian, Lebanese, Sri Lankan, Armenian, Hmong, Vietnamese.

But it is absurd to argue that racism is the sole reason for every racial difference in outcome in the extraordinarily diverse and constantly shifting racial demographics of New York City or the US

It’s true, of course, that historical injustices have deeply hurt African-Americans in particular in hobbling opportunity, which is why African-Americans who are descendants of slaves should be treated as an entirely separate case from all other racial categories. No other group has experienced anything like the toll of slavery, segregation and brutality that African-Americans have. This discrimination was enforced by the state and so the state has an obligation to make things right. 

You can argue that these groups are immigrants and self-selecting for those with higher IQs, education, motivation, and drive. It’s true. But notice that this argument cannot be deployed under the Kendi test: any inequality is a result of racism, remember?

In fact, to reduce all this complexity to a quick, crude check of race and sex to identify your fellow American is a kind of new racism itself.

It has taken off because we find it so easy to slip back into crude generalizations.

for all those reasons, attempting to categorize people in the crudest racial terms, and social engineering them into a just society where every institution looks like every other one, is such a nightmare waiting to happen. It’s a brutal, toxic, racist template being imposed on a dazzling varied and constantly shifting country.

this explicit reintroduction of crude racism under the guise of antiracism is already happening. How many institutions will it tear apart, and how much racial resentment will it foment, before it’s done? 

this cannot mean a return to the status quo ante. That would ignore the lessons of the 21st century — that neoconservatism’s desire to rule the world is a fantasy, and that zombie Reagonomics has been rendered irrelevant by its own success and unintended failures

What the right needs to do, quite simply, is to seize the mantle of cultural conservatism while moving sharply left on economics.

Here’s the gist of a platform I think could work. The GOP should drop the tax cut fixation, raise taxes on the wealthy, and experiment with UBI

It needs a workable healthcare policy which can insure everyone in the country, on Obamacare private sector lines. (Yes, get the fuck over Obamacare. It’s the most conservative way to achieve universal access to healthcare we have.

It has to promote an agenda of lower immigration as a boon to both successful racial integration and to raising working class wages.

It needs finally to acknowledge the reality of climate change and join the debate about how, rather than whether, to tackle it.

It has to figure out a China policy that is both protective of some US industries and firm on human rights.

It needs to protect religious freedom against the incursions of the cultural left.

And it needs to become a place where normie culture can live and thrive, where acknowledgment of America’s past failures doesn’t exclude pride in America’s great successes, and where the English language can still be plainly used.

No big need to change on judges (except finding qualified ones); and no reason either to lurch back to worrying about deficits in the current low-inflation environment.

I believe this right-of-center pragmatism has a great future. It was the core message behind the British Tories’ remarkable success in the 2019 election

The trouble, of course, is that GOP elites would have a hell of a time achieving this set of policies with its current membership. Damon Linker has a terrific piece about the problem of Republican voters most of whom “remain undaunted in their conviction that politics is primarily about the venting of grievances and the trolling of opponents. The dumber and angrier and more shameless, the better.”

I see no reason why someone else couldn’t shift it yet again — not back to pre-Trump but forward to a new fusion of nationalist realism, populist economics, and cultural conservatism. By cultural conservatism I don’t mean another round of the culture wars — but a defense of pride in one’s country, respect for tradition, and social stability. There is also, I suspect, a suppressed but real desire for the normality and calmness that Trump has eviscerated.

What I was trying to argue is that the roots of critical theory are fundamentally atheist, are very much concerned with this world alone, and have no place for mercy or redemption or the individual soul.

Christians who think they can simply adopt both are being somewhat naive. And yes, I feel the same way about “liberation theology” as well, however sympathetic the Pope now is.

It seems to me the logical outcome of a broad application of critical theory will be a wider revival of white supremacy. Where there’s no possibility of redemption, resistance becomes inevitable.

A contest of ideas shouldn’t be used to legitimize disinformation. And I think it’s often taken advantage of. And I think at great news organizations, the mission really should be to introduce fact to disperse doubt — not to sow doubt, to obscure fact, if you will.

In 2017, President Trump’s praise for white supremacists in Charlottesville, Va., as “very fine people” spurred James Murdoch to give $1 million to the Anti-Defamation League. In an email to friends obtained by The New York Times, Mr. Murdoch rebuked Mr. Trump and wrote: “I can’t even believe I have to write this: standing up to Nazis is essential; there are no good Nazis. Or Klansmen, or terrorists.”

In January, James and his wife, Kathryn, expressed “frustration” about News Corp’s peddling of climate change denialism in the face of apocalyptic Australian wildfires that incinerated 46 million acres. Fox nighttime anchors picked up a false story line about arson from The Australian, a Murdoch-owned newspaper in Oz.

So it wasn’t possible to change News Corp from the inside?“I think there’s only so much you can do if you’re not an executive, you’re on the board, you’re quite removed from a lot of the day-to-day decisions, obviously,” he said. “And if you’re uncomfortable with those decisions, you have to take stock of whether or not you want to be associated and can you change it or not. I decided that I could be much more effective outside.”

Friends say that James has been on a collision course with his family for 15 years. His evolution has been profoundly influenced by his wife, a former communications executive. He is, as one friend puts it, “living much more in his own skin, realizing his better angels and his better instincts.”

But when your last name is Murdoch and those billions sloshing around in your bank account come from a juggernaut co-opting governments across the English-speaking world and perpetuating climate-change denial, nativism and Sean Hannity, can you ever start fresh? As a beneficiary of his family’s trust, James is still reaping profits from Rupert Murdoch’s assets. Can he be the anti-venom?

Murdoch watchers across media say James is aligned with his sister Elisabeth and his half sister, Prudence, even as he is estranged from his father and brother.

When Rupert, 89, finally leaves the stage and his elder children take over, that could make three votes in the family trust against one

Is there still time to de-Foxify Fox News — labeled a “hate-for-profit racket” by Elizabeth Warren — and other conservative News Corp outlets? Would Fox and its kin — downscale, feral creatures conjured by Rupert to help the bottom line — be the huge moneymakers they are if they went straight?

He is particularly excited about investing in start-ups created to combat fake news and the spread of disinformation, having found the proliferation of deep fakes “terrifying” because they “undermine our ability to discern what’s true and what’s not” and it “is only at the beginning as far as I can tell.”

He’s funding a research program to study digital manipulation of societies, hoping to curtail “the use of technology to promulgate totalitarianism’’ and undermine democracies.

“So everything from the use of mass surveillance, telephone networks, 5G, all that stuff, domestically in a country like China, for example,” he said.

I wonder if this is some sort of expiation, given all the disinformation that News Corp has spewed.

when I talked to Kathryn Murdoch over Zoom from their farm in Connecticut, where they live with their three teenagers, chickens and sheep, she was more direct about the issue of using money made from disinformation to combat disinformation.

“I think that what’s important about what we’re doing is that we’re in control of ourselves,” she said, adding: “I’m in control of what I do, he is in control of what he does. We should be held accountable for those things. It’s very hard to be held accountable for things that other people do or are in control of. And I think that’s what was untenable.”

Their foundation, Quadrivium, has supported voter participation, democracy reform and climate change projects. “I never thought that we would actually be at the point where we would have climate change effects and people would still be denying it,” Ms. Murdoch said.

Mr. Murdoch donated to Pete Buttigieg in the primary, and the couple has given $1.23 million to Joe Biden. So that’s who he’ll be voting for in November then? “Hell yes,” he said with a smile.

I noted to Ms. Murdoch that the effect of News Corp on the world is astounding when you think about it, from Brexit to Trump to the Supreme Court we may be heading toward.

After so much time in the executive suite, Mr. Murdoch seems genuinely excited to be in a smaller shop. He said last year, just for the hell of it, he thought of becoming an architect, going back to school.

“The outside world,” he continued, “it looks at you and says, ‘Well, these are the runners and riders. This person is up and down and this is success and this is failure.’ I think that that has to come much more from yourself. I’m incredibly grateful to be able to be just a totally free agent.”

I wondered what he made of Fox and Mr. Trump playing down the coronavirus, even after the president was hospitalized.“Look, you do worry about it and I think that we’re in the middle of a public health crisis,” Mr. Murdoch said. “Climate is also a public health crisis.” He continued: “Whatever political spin on that, if it gets in the way of delivering crucial public health information, I think is pretty bad.”

He added that Mr. Trump’s likening Covid-19 to the flu has been “his message from Day 1,” and is “craziness.” He thinks that “companies have a responsibility to their customers and their communities” and “that responsibility shouldn’t be compromised by political point scoring, that’s for sure.”

“I’m just concerned that the leadership that we have, to me, just seems characterized by callousness and a level of cruelty that I think is really dangerous and then it infects the population,” he said, referring to the Trump administration. “It’s not a coincidence that the number of hate crimes in this country are rising over the last three years for the first time in a long time.”

With Mr. Trump and Fox, who is the dog and who is the tail?“It looks to me, anyway, like it’s going to be a hard thing to understand because it probably goes back and forth,’’ he said. “I don’t think you’re going to get one pristine, consistent analysis of that phenomenon.”

Confirm or Deny

Most of your success has come from hard work, not luck.Isn’t that what they say — the harder you work, the luckier you get?

Every law embodies a particular hypothesis about how the world works, a hope that if you pull on levers A and B, then outcomes C and D will result

Democrats hope to create an economy where the government doesn’t just help Americans buy green technologies; it also helps nurture the industries that produce that technology.

The idea is this: The era of passive, hands-off government is over. The laws embrace an approach to governing the economy that scholars call “industrial policy,” a catch-all name for a wide array of tools and tactics that all assume the government can help new domestic industries get started, grow, and reach massive scale.

If “this country used to make things,” as the saying goes, and if it wants to make things again, then the government needs to help it. And if the country believes that certain industries bestow a strategic advantage, then it needs to protect them against foreign interference.

From its founding to the 1970s, the country had an economic doctrine that was defined by its pragmatism and the willingness of its government to find new areas of growth.

It’s more like a toolbox of different approaches that act in concert to help push technologies to grow and reach commercial scale. The IRA and the two other new laws prefer four tools in particular.

“Yes, there was an ‘invisible hand,’” Stephen Cohen and Brad DeLong write in their history of the topic, Concrete Economics. “But the invisible hand was repeatedly lifted at the elbow by the government, and placed in a new position from where it could go on to perform its magic.”

That pragmatism faded in the 1980s, when industrial policy became scorned as one more instance of Big Government coming in to pick so-called winners and losers.

The two other large bills passed by this Congress—the $1 trillion bipartisan infrastructure law and the CHIPS and Science Act—make down payments on the future as well; both laws, notably, were passed by bipartisan majorities.

it is in the IRA that these general commitments become specific, and therefore transformative.

Since the 1980s, when Congress has wanted to spur technological progress, it has usually thrown money exclusively at R&D. We have had a science policy, not an industrial policy

inextricable from that turn is Washington’s consuming anxiety over China’s rise—and China has embraced industrial policy.

although not a single Republican voted for the IRA, its wager is not especially partisan or even ideological.

the demonstration project. A demonstration project helps a technology that has previously existed only in the lab get out in the real world for the first time

supply-push policies. As the name suggests, these tools “push” on the supply side of an industry by underwriting new factories or assuring that those factories have access to cheap inputs to make things.

demand-pull policies, which create a market for whatever is coming out of those new factories. The government can “pull” on demand by buying those products itself or by subsidizing them for consumers.

protective policies, meant to insulate industries—especially new ones that are still growing—from foreign interference

Although both parties have moved to embrace industrial policy, Democrats are clearly ahead of their Republican colleagues. You can see it in their policy: While the bipartisan infrastructure law sets up lots of demonstration projects, and the CHIPS Act adopts some supply-push and protectionist theory, only the IRA uses all four tools.

In order to stop climate change, experts believe, the United States must do three things: clean up its power grid, replacing coal and gas power plants with zero-carbon sources; electrify everything it can, swapping fossil-fueled vehicles and boilers with electric vehicles and heat pumps; and mop up the rest, mitigating carbon pollution from impossible-to-electrify industrial activities. The IRA aims to nurture every industry needed to realize that vision.

Hydrogen and carbon removal are going to benefit from nearly every tool the government has. The bipartisan infrastructure law will spend more than $11 billion on hydrogen and carbon-removal “hubs,” huge demonstration projects

These hubs will also foster geographic concentration, the economic idea that when you put lots of people working on the same problem near one another, they solve it faster. You can see such clustering at work in San Francisco’s tech industry, and also in China, which now creates hubs for virtually every activity that it wants to dominate globally—even soccer.

Then the IRA will take over and deploy some good ol’ supply push and demand pull. It includes new programs to underwrite new hydrogen factories; on the demand side, a powerful new tax credit will pay companies for every kilogram of low-carbon hydrogen that they produce

Another tax credit will boost the demand of carbon removal by paying firms a $180 bounty for trapping a ton of carbon dioxide and pumping it undergroun

Today, not only does China make most batteries worldwide; it alone makes the tools that make the batteries, Nathan Iyer, an analyst at RMI, a nonpartisan energy think tank, told me. This extreme geographic concentration—which afflicts not only the battery industry but also the solar-panel industry—could slow down the energy transition and make it more expensive

the new tax credit is also supply-minded, arguably even protectionist. Under the new scheme, very few electric cars and trucks will immediately qualify for that full $7,500 subsidy; it will go only toward vehicles whose batteries are primarily made in North America and where a certain percentage of minerals are mined and processed in the U.S. or one of its allies. Will these policies accelerate the shift to EVs? Well, no, not immediately. But the idea is that by boosting domestic production of EVs, batteries will become cheaper and more abundant—and the U.S. will avoid subsidizing one of China’s growth industries.

Right now, next to no solar panels are made in the U.S., even though the technology was invented here. The IRA endeavors to change that by—you guessed it—a mix of supply-push, demand-pull, and protectionist policies. Under the law, the government will underwrite new factories to make every subcomponent of the solar supply chain; then it will pay those factories for every item that they produce

“It’s realistic that within four to five years, [U.S. solar manufacturers] could completely meet domestic demand for solar,” Scott Moskowitz, the head of public affairs for the solar manufacturer Q CELLS, told me.

In each of these industries, you’ll notice that the government isn’t only subsidizing factories; it is actually paying them to operate. That choice, which is central to the IRA’s approach, is “really defending against the mistakes of the 2009 bill,” Iyer told me. In its stimulus bill passed during the Great Recession, the Obama administration tried to do green industrial policy, underwriting new solar-panel factories across the country. But then Chinese firms began exporting cheap solar panels by the millions, saturating domestic demand and leaving those sparkly new factories idle

So many other industries will also be touched by these laws. There’s a new program to nurture a low-carbon aviation-fuel industry in the U.S. (Long-distance jet travel is one of those climate problems that nobody knows how to solve yet.)

the revelation of the IRA is that decarbonizing the United States may require re-industrializing it. A net-zero America may have more refineries, more factories, and more goods production than a fossil-fueled America—while also having cheaper cars, healthier air, and fewer natural disasters. And once the U.S. gets there, then it can keep going: It can set an example for the world that a populous, affluent country can reduce its emissions while enjoying all the trappings of modernity,

There are a slew of policies meant to grow and decarbonize the U.S. industrial sector; every tax credit pays out a bonus if you use U.S.-made steel, cement, or concrete. “You would need thousands and thousands of words to capture the industries that will be transformed by this,” Josh Freed, the climate and energy leader at Third Way, a center-left think tank, told me.

Five EVs were sold in China last year for every one EV sold in the United States; that larger domestic market will provide a significant economy of scale when Chinese EV makers begin exporting their cars abroad. For that reason and others, many people in China are “deeply skeptical” that the U.S. can catch up with its lead,

We are about to have a huge new set of vested interests who want the economy to be clean and benefit from that. We’ve literally never had that before,” Freed told me.

“This is going to change everything,” he said

that is the IRA’s biggest idea, its biggest hypothesis: that America can improve its standard of living and preserve its global preeminence while ruthlessly eliminating carbon pollution; that climate change, actually, doesn’t change everything, and that in fact it can be addressed by changing as little as possible.

This hypothesis has already proved itself out in one important way, which is that the IRA passed, and the previous 30 years of climate proposals did not. Now comes the real test.

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.

New York is conducting more testing per capita than countries such as South Korea, which may partially explain the spike in numbers.

“We need to be smarter about this … we need the federal help and we need it now,” he said, calling on Donald Trump to utilise the Defense Production Act (DPA) to force companies to produce medical equipment and provide them the necessary financial support to do so. So far, the administration has relied on volunteer efforts that Cuomo said were not appropriate for the urgency of crisis.

“What happens to New York is gonna wind up happening to California, and Washington state and Illinois. It’s just a matter of time. We’re just getting there first,” Cuomo said. “Look at us today … where we are today, you will be in four weeks or five weeks or six weeks. We are your future.”

Speaking later in the day, the vice-president, Mike Pence, said the Federal Emergency Management Agency (Fema) had sent New York 2,000 ventilators and plans to send 2,000 more on Wednesday.

He also rejected the suggestion, floated by the president in recent days, that advice from public health officials could be rowed back in a matter of weeks to protect the US economy, saying that sensible steps in both areas could be made now without increasing risk to more vulnerable people in society.

uomo’s urgent message comes as a spokesperson for the World Health Organization (WHO) warned the US has the potential to become the next global hotspot for the pandemic. The country already has more than 46,000 confirmed Covid-19 cases, resulting in close to 600 deaths, according to Johns Hopkins University School of Medicine.

With cases surging, Cuomo said new projections estimate his state will need 140,000 hospital beds once the crisis reaches its apex, which could happen within 14 to 21 days instead of in May as previously forecast.

The outbreak has led to a dramatic change in America’s social fabric: as of Monday, the New York Times estimated that more than 158 million people in 16 states would soon be under strict orders to stay at home – approaching half of the population.

The shift in messaging came after James Bullard, president of the Federal Reserve Bank of St Louis, predicted unemployment could reach 30% nationwide, according to Bloomberg News, and as negotiations for a coronavirus stimulus package dragged on for days in the US Senate. For weeks, suddenly jobless workers have been imploring the government for help, and Trump is staring down what could become a historic recession during an election year.

Even still, medical experts have cautioned against such an abrupt restart to the economy while the virus continues to spread exponentially. “You can’t call off the best weapon we have, which is social isolation, even out of economic desperation, unless you’re willing to be responsible for a mountain of deaths,” Arthur Caplan, founding head of the division of medical ethics at NYU School of Medicine, told the New York Times. “Can’t we try to put people’s lives first for at least a month?”

“If you ask the American people to choose between public health and the economy, then it’s no contest,” Cuomo said. “No American is going to say, accelerate the economy at the cost of human life, because no American is going to say how much a life is worth.”

In 1981, Huntington predicted that the next moral convulsion would hit America around the second or third decade of the 21st century—that is, right about now.

Trump is the final instrument of this crisis, but the conditions that brought him to power and make him so dangerous at this moment were decades in the making, and those conditions will not disappear if he is defeated.

Social trust is a measure of the moral quality of a society—of whether the people and institutions in it are trustworthy, whether they keep their promises and work for the common g

When people in a society lose faith or trust in their institutions and in each other, the nation collapses.

This is an account of how, over the past few decades, America became a more untrustworthy society

under the stresses of 2020, American institutions and the American social order crumbled and were revealed as more untrustworthy still

We had a chance, in crisis, to pull together as a nation and build trust. We did not. That has left us a broken, alienated society caught in a distrust doom loop.

The Baby Boomers grew up in the 1950s and ’60s, an era of family stability, widespread prosperity, and cultural cohesion. The mindset they embraced in the late ’60s and have embodied ever since was all about rebelling against authority, unshackling from institutions, and celebrating freedom, individualism, and liberation.

The emerging generations today enjoy none of that sense of security. They grew up in a world in which institutions failed, financial systems collapsed, and families were fragile. Children can now expect to have a lower quality of life than their parents, the pandemic rages, climate change looms, and social media is vicious. Their worldview is predicated on threat, not safety.

Thus the values of the Millennial and Gen Z generations that will dominate in the years ahead are the opposite of Boomer values: not liberation, but security; not freedom, but equality; not individualism, but the safety of the collective; not sink-or-swim meritocracy, but promotion on the basis of social justice

A new culture is dawning. The Age of Precarity is here.

I’ve spent my career rebutting the idea that America is in decline, but the events of these past six years, and especially of 2020, have made clear that we live in a broken nation. The cancer of distrust has spread to every vital organ.

Those were the days of triumphant globalization. Communism was falling. Apartheid was ending. The Arab-Israeli dispute was calming down. Europe was unifying. China was prospering. In the United States, a moderate Republican president, George H. W. Bush, gave way to the first Baby Boomer president, a moderate Democrat, Bill Clinton.

The stench of national decline is in the air. A political, social, and moral order is dissolving. America will only remain whole if we can build a new order in its place.

The American economy grew nicely. The racial wealth gap narrowed. All the great systems of society seemed to be working: capitalism, democracy, pluralism, diversity, globalization. It seemed, as Francis Fukuyama wrote in his famous “The End of History?” essay for The National Interest, “an unabashed victory for economic and political liberalism.”

Nations with low social trust—like Brazil, Morocco, and Zimbabwe—have struggling economies.

We think of the 1960s as the classic Boomer decade, but the false summer of the 1990s was the high-water mark of that ethos

The first great theme of that era was convergence. Walls were coming down. Everybody was coming together.

The second theme was the triumph of classical liberalism. Liberalism was not just a philosophy—it was a spirit and a zeitgeist, a faith that individual freedom would blossom in a loosely networked democratic capitalist world. Enterprise and creativity would be unleashed. America was the great embodiment and champion of this liberation.

The third theme was individualism. Society flourished when individuals were liberated from the shackles of society and the state, when they had the freedom to be true to themselves.

For his 2001 book, Moral Freedom, the political scientist Alan Wolfe interviewed a wide array of Americans. The moral culture he described was no longer based on mainline Protestantism, as it had been for generations

Instead, Americans, from urban bobos to suburban evangelicals, were living in a state of what he called moral freedom: the belief that life is best when each individual finds his or her own morality—inevitable in a society that insists on individual freedom.

moral freedom, like the other dominant values of the time, contained within it a core assumption: If everybody does their own thing, then everything will work out for everybody.

This was an ideology of maximum freedom and minimum sacrifice.

It all looks naive now. We were naive about what the globalized economy would do to the working class, naive to think the internet would bring us together, naive to think the global mixing of people would breed harmony, naive to think the privileged wouldn’t pull up the ladders of opportunity behind them

Over the 20 years after I sat with Kosieva, it all began to unravel. The global financial crisis had hit, the Middle East was being ripped apart by fanatics. On May 15, 2011, street revolts broke out in Spain, led by the self-declared Indignados—“the outraged.” “They don’t represent us!” they railed as an insult to the Spanish establishment. It would turn out to be the cry of a decade.

Millennials and members of Gen Z have grown up in the age of that disappointment, knowing nothing else. In the U.S. and elsewhere, this has produced a crisis of faith, across society but especially among the young. It has produced a crisis of trust.

Social trust is a generalized faith in the people of your community. It consists of smaller faiths. It begins with the assumption that we are interdependent, our destinies linked. It continues with the assumption that we share the same moral values. We share a sense of what is the right thing to do in different situations

gh-trust societies have what Fukuyama calls spontaneous sociability. People are able to organize more quickly, initiate action, and sacrifice for the common good.

When you look at research on social trust, you find all sorts of virtuous feedback loops. Trust produces good outcomes, which then produce more trust. In high-trust societies, corruption is lower and entrepreneurship is catalyzed.

Higher-trust nations have lower economic inequality, because people feel connected to each other and are willing to support a more generous welfare state.

People in high-trust societies are more civically engaged. Nations that score high in social trust—like the Netherlands, Sweden, China, and Australia—have rapidly growing or developed economies.

Renewal is hard to imagine. Destruction is everywhere, and construction difficult to see.

As the ethicist Sissela Bok once put it, “Whatever matters to human beings, trust is the atmosphere in which it thrives.”

During most of the 20th century, through depression and wars, Americans expressed high faith in their institutions

In 1964, for example, 77 percent of Americans said they trusted the federal government to do the right thing most or all of the time.

By 1994, only one in five Americans said they trusted government to do the right thing.

Then came the Iraq War and the financial crisis and the election of Donald Trump. Institutional trust levels remained pathetically low. What changed was the rise of a large group of people who were actively and poi

sonously alienated—who were not only distrustful but explosively distrustful. Explosive distrust is not just an absence of trust or a sense of detached alienation—it is an aggressive animosity and an urge to destroy. Explosive distrust is the belief that those who disagree with you are not just wrong but illegitimate

In 1997, 64 percent of Americans had a great or good deal of trust in the political competence of their fellow citizens; today only a third of Americans feel that way.

In most societies, interpersonal trust is stable over the decades. But for some—like Denmark, where about 75 percent say the people around them are trustworthy, and the Netherlands, where two-thirds say so—the numbers have actually risen.

In America, interpersonal trust is in catastrophic decline. In 2014, according to the General Social Survey conducted by NORC at the University of Chicago, only 30.3 percent of Americans agreed that “most people can be trusted,”

Today, a majority of Americans say they don’t trust other people when they first meet them.

There’s evidence to suggest that marital infidelity, academic cheating, and animal cruelty are all on the rise in America, but it’s hard to directly measure the overall moral condition of society—how honest people are, and how faithful.

Trust is the ratio between the number of people who betray you and the number of people who remain faithful to you. It’s not clear that there is more betrayal in America than there used to be—but there are certainly fewer faithful supports around people than there used to be.

Hundreds of books and studies on declining social capital and collapsing family structure demonstrate this. In the age of disappointment, people are less likely to be surrounded by faithful networks of people they can trust.

Black Americans have high trust in other Black Americans; it’s the wider society they don’t trust, for good and obvious reasons

As Vallier puts it, trust levels are a reflection of the moral condition of a nation at any given time.

high national trust is a collective moral achievement.

High national distrust is a sign that people have earned the right to be suspicious. Trust isn’t a virtue—it’s a measure of other people’s virtue.

Unsurprisingly, the groups with the lowest social trust in America are among the most marginalized.

Black Americans have been one of the most ill-treated groups in American history; their distrust is earned distrust

In 2018, 37.3 percent of white Americans felt that most people can be trusted, according to the General Social Survey, but only 15.3 percent of Black Americans felt the same.

People become trusting when the world around them is trustworthy. When they are surrounded by people who live up to their commitments. When they experience their country as a fair place.

In 2002, 43 percent of Black Americans were very or somewhat satisfied with the way Black people are treated in the U.S. By 2018, only 18 percent felt that way, according to Gallup.

The second disenfranchised low-trust group includes the lower-middle class and the working poor.

this group makes up about 40 percent of the country.

“They are driven by the insecurity of their place in society and in the economy,” he says. They are distrustful of technology and are much more likely to buy into conspiracy theories. “They’re often convinced by stories that someone is trying to trick them, that the world is against them,”

the third marginalized group that scores extremely high on social distrust: young adults. These are people who grew up in the age of disappointment. It’s the only world they know.

In 2012, 40 percent of Baby Boomers believed that most people can be trusted, as did 31 percent of members of Generation X. In contrast, only 19 percent of Millennials said most people can be trusted

Seventy-three percent of adults under 30 believe that “most of the time, people just look out for themselves,” according to a Pew survey from 2018. Seventy-one percent of those young adults say that most people “would try to take advantage of you if they got a chance.

A mere 10 percent of Gen Zers trust politicians to do the right thing.

Only 35 percent of young people, versus 67 percent of old people, believe that Americans respect the rights of people who are not like them.

Fewer than a third of Millennials say America is the greatest country in the world, compared to 64 percent of members of the Silent Generation.

“values and behavior are shaped by the degree to which survival is secure.” In the age of disappointment, our sense of safety went away

Some of this is physical insecurity: school shootings, terrorist attacks, police brutality, and overprotective parenting at home

the true insecurity is financial, social, and emotional.

By the time the Baby Boomers hit a median age of 35, their generation owned 21 percent of the nation’s wealth

First, financial insecurity

As of last year, Millennials—who will hit an average age of 35 in three years—owned just 3.2 percent of the nation’s wealth.

Next, emotional insecurity:

fewer children growing up in married two-parent households, more single-parent households, more depression, and higher suicide rates.

Then, identity insecurity.

All the traits that were once assigned to you by your community, you must now determine on your own: your identity, your morality, your gender, your vocation, your purpose, and the place of your belonging. Self-creation becomes a major anxiety-inducing act of young adulthood.

liquid modernity

Finally, social insecurity.

n the age of social media our “sociometers”—the antennae we use to measure how other people are seeing us—are up and on high alert all the time. Am I liked? Am I affirmed?

Danger is ever present. “For many people, it is impossible to think without simultaneously thinking about what other people would think about what you’re thinking,” the educator Fredrik deBoer has written. “This is exhausting and deeply unsatisfying. As long as your self-conception is tied up in your perception of other people’s conception of you, you will never be free to occupy a personality with confidence; you’re always at the mercy of the next person’s dim opinion of you and your whole deal.”

In this world, nothing seems safe; everything feels like chaos.

Distrust sows distrust. It produces the spiritual state that Emile Durkheim called anomie, a feeling of being disconnected from society, a feeling that the whole game is illegitimate, that you are invisible and not valued, a feeling that the only person you can really trust is yourself.

People plagued by distrust can start to see threats that aren’t there; they become risk averse

Americans take fewer risks and are much less entrepreneurial than they used to be. In 2014, the rate of business start-ups hit a nearly 40-year low. Since the early 1970s, the rate at which people move across state lines each year has dropped by 56 percent

People lose faith in experts. They lose faith in truth, in the flow of information that is the basis of modern society. “A world of truth is a world of trust, and vice versa,”

In periods of distrust, you get surges of populism; populism is the ideology of those who feel betrayed

People are drawn to leaders who use the language of menace and threat, who tell group-versus-group power narratives. You also get a lot more political extremism. People seek closed, rigid ideological systems that give them a sense of security.

fanaticism is a response to existential anxiety. When people feel naked and alone, they revert to tribe. Their radius of trust shrinks, and they only trust their own kind.