Group items tagged

The modern GE was built by Jack Welch, the youngest CEO and chairman in company history when he took over in 1981. He ran it for 20 years, becoming the rare CEO who was also a household name, praised for his strategic and operational mastery.

their most obvious problem. GE couldn’t live without GE Capital, still so big it was essentially the nation’s seventh largest bank. But investors couldn’t live with GE Capital and its unshakable shadow of risk, either.

it worked more like a collection of businesses under the protection of a giant bank. As the financial sector came to drive more of the U.S. economy, GE Capital, the company’s finance arm, powered more of the company’s growth. At its height, Capital accounted for more than half of GE’s profits. It rivaled the biggest banks in the country, competed with Wall Street for the brightest M.B.A.s and employed hundreds of bankers.

The industrial spine of the company gave GE a AAA credit rating that allowed it to borrow money inexpensively, giving it an advantage over banks, which relied on deposits. The cash flowed up to headquarters where it powered the development of new jet engines and dividends for shareholders.

Capital also gave General Electric’s chief executives a handy, deep bucket of financial spackle with which to smooth over the cracks in quarterly earnings reports and keep Wall Street happy

GE shares were trading at 40 times its earnings when Welch retired in 2001, more than double where it had historically. And much of those profits were coming from deep within Capital, not the company’s factories.

When the financial crisis hit, Capital fell back to earth, taking GE’s share price and Immelt with it. The stock closed as low as $6.66 in March 2009. General Electric was on the brink of collapse. The market for short-term loans, the lifeblood of GE Capital, had frozen, and there was little in the way of deposits to fall back on. The Federal Reserve stepped in to save it after an emergency plea from Immelt.

the near-death experience taught investors to think of GE like a bank, a stock always vulnerable to another financial collapse

At its peak, General Electric was the most valuable company in the U.S., worth nearly $600 billion in August 2000. That year, GE’s third of a million employees operated 150 factories in the U.S., and another 176 in 34 other countries. Its pension plan covered 485,000 people.

What if the GE Jack Welch built didn’t work any more?

Cracks in the performance of the company’s industrial lines—its power turbines, jet engines, locomotives and MRI machines—would now be plain to see, some executives worried, without Capital’s cash to help cover the weak quarters and pay the sacrosanct dividend

Most of the shortfall came from its service contracts, which should have been the source of the easiest profits. Instead, the heart of the industrial business was hollow. And its failure was about to tip the entire company into crisis.

Former colleagues compared him to Bill Clinton because of his magnetic ability to hold the focus of a room. He sounded like a leader. He was a natural salesman.

Immelt was so confident in GE’s managerial excellence that he projected a sunny vision for the company’s future that didn’t always match reality. He was aware of the challenges, but he wanted his people to feel like they were playing for a winning team. That often left Immelt, in the words of one GE insider, trying to market himself out of a math problem.

Alstom’s problems hadn’t gone away, but now its stock was cheaper, and Immelt saw the makings of a deal that fit perfectly with his vision for reshaping his company. GE would essentially swap Capital, the cash engine that no longer made sense, for a new one that could churn out profits each quarter in the reliable way that industrial companies were supposed to.

To the dismay of some involved, GE’s bid crept upward, from the €30 a share that the power division’s deal team already believed was too high, to roughly €34, or almost $47. Immelt and Kron met one-on-one, and the deal team realized the game was over. The principals had shaken hands.

The visions for the present and the future were both fundamentally flawed. As GE’s research department was preparing white papers heralding “The Age of Gas,” the world was entering a multiyear decline in the demand for new gas power plants and for the electricity that made them profitable.

When advisers determined that the concessions to get the deal approved might have grown costly enough to trigger a provision allowing GE to back out, some in the Power business quietly celebrated, confiding in one another that they assumed management would abandon the deal. But Immelt and his circle of closest advisers wanted it done. That included Steve Bolze, the man who ran it and hoped someday to run all of General Electric.

“Steve’s our guy,” McElhinney said in one meeting. If Bolze was elevated to CEO, those behind him in Power would rise too. “Get on board,” he said. “We have to make the numbers.”

Immelt, trapped in Welch’s long shadow, craved a bold move to shock his company out of the doldrums that had plagued his tenure. It was time for GE to be reinvented again.

In the dry language of accounting in which he was so fluent, Flannery was declaring a pillar of Immelt’s pivot had failed: GE had been sending money out the door to repurchase its stock and pay dividends but wasn’t bringing in enough from its regular operations to cover them. It wasn’t sustainable. Buybacks and dividends are generally paid out of leftover funds.

when GE spun off Genworth, there was a chunk of the business, long-term-care insurance, that lingered. Policies designed to cover expenses like nursing homes and assisted living had proved to be a disaster for insurers who had drastically underestimated the costs

The bankers didn’t think the long-term-care business could be part of the Genworth spinoff. To make the deal more attractive, GE agreed to cover any losses. This insurance for insurers covered about 300,000 policies by early 2018, about 4% of all such policies written in the country. Incoming premiums weren’t covering payouts.

Two months after Miller flagged the $3 billion, it was clear the problem was a great deal larger. GE was preparing for it to be more than $6 billion and needed to come up with $15 billion in reserves regulators required it to have to cover possible costs in the future. The figure was gigantic. By comparison, even after the recent cut, GE’s annual dividend cost $4 billion.

JP Morgan analyst Steve Tusa, who led the pack in arguing that GE was harboring serious problems, removed his sell rating on the stock this week. GE’s biggest skeptic still thinks the businesses are broken but the risks are now known. The stock climbed back above $7 on Thursday, but is down more than 50% for the year and nearly 90% from its 2000 zenith.

The people who oppose Trump make a big error: “Let’s Get Togetherism.” This is the belief that if we can only have a civil conversation between red and blue, then everything will be better. But you can’t destroy a moral vision with a process. You need a counter-moral vision.

This is like 1917, a clash of political, moral, economic and social ideologies all rolled into one.

I think America’s traditional biblical ethic is still lurking somewhere in the national DNA. But there has to be a leader who can restore it to life.

At midcentury, the common critique was not of too little bipartisanship, but of too much

Two currents, one intellectual and one cultural, helped to undermine this model and presage the marriage of party and ideology we have today.

The intellectual current was the rise of the “responsible party” theory of government, advocated by political scientists who argued that the lack of clarity between parties “stifled progress while blurring accountability to the voters,”

The cultural trend was that as voters became better educated and more economically stable, they wanted something more out of politics than the patronage provided by the old political machines

This led a new generation of solidly middle-class and young activists in both parties to turn away from the pragmatic politics of backroom bargaining and technocratic tinkering of their elders and toward a new front of ideological battle, beginning with civil rights.

The Goldwaterites, for example, rightly believed that they could appeal to southern conservative Democrats over their shared distaste for civil rights legislation and defeat the far less organized urban-oriented liberal moderates, who Rosenfeld importantly notes were too top heavy with elected officials and intellectuals and “devoid of grassroots strength.”

Among Democrats, the energy for aligning party with principle came from “young, educated New Deal liberals, motivated largely by national issues, [who] forged alliances with organized labor and racial minorities to square off against sclerotic, generally non-ideological Democratic organizations.” Civil rights was their cri de coeur

With the arrival of the “Watergate Babies” after the 1974 election, congressional Democrats enacted new rules to give both the caucus and the speaker more power to overcome the conservatives. These rules were pushed by liberal outside groups, most prominently Common Cause. Later, Newt Gingrich would further centralize power when he became speaker, bringing American politics a step even closer to the responsible-party vision.

Richard Nixon opined in 1959, “It would be a great tragedy if we had our two major political parties divided on what we would call a conservative-liberal line. . . . [O]ne of the attributes of our political system has been that we have avoided generally violent swings in administrations from one extreme to the other.”

As a mode of governing, non-ideological politics worked well with the technocratic solutionism of President John F. Kennedy, who proudly told Yale graduates in 1962, “The central domestic issues of our time relate not to basic clashes of philosophy or ideology but to ways and means of reaching common goals.”

Consensus politics could work when the substance of party politics was thick with issues of consensus, in which debates over means dominated debates over ends. Once ends came to be more important than means, politics changed. Pragmatic incrementalism was no match for moral urgency.

Since the 1980s, the ideological activists have remained dominant in both parties, steadily weeding out dissenting holdovers from an earlier era while enforcing more and more programmatic unity, an iterative and ultimately generational project. Even the “New Democrats” of the 1990s “pitched moderate programmatic initiatives in explicitly partisan terms.”

Rosenfeld’s history lesson gives us two key takeaways.

modern advocates of “bipartisanship” remember that bipartisanship was not all lollipops and roses. It fostered a political system in which voters lacked meaningful choices, and it stymied civil rights.

it’s not clear how we could simply reverse six decades of organizational and coalitional transformation. The party organizations of the 1950s reflected a very different world, in which most politics was local and there were few highly educated, activist voters

an optimistic takeaway from the book is that change is possible through deliberate action. Just as activists half a century ago set in motion a new vision for a party system, so too might today’s activists set in motion their own vision for a new party politics.

U.S. history is often taught and popularly understood through the eyes of its great men, who are seen as either heroic or tragic figures in a global struggle for human freedom.

The 1619 Project, named for the date of the first arrival of Africans on American soil, sought to place “the consequences of slavery and the contributions of black Americans at the very center of our national narrative.” Viewed from the perspective of those historically denied the rights enumerated in America’s founding documents, the story of the country’s great men necessarily looks very different.

given the stature of the historians involved, the letter is a serious challenge to the credibility of the 1619 Project

he letter sent to the Times says, “We applaud all efforts to address the foundational centrality of slavery and racism to our history,” but then veers into harsh criticism of the 1619 Project. The letter refers to “matters of verifiable fact” that “cannot be described as interpretation or ‘framing’” and says the project reflected “a displacement of historical understanding by ideology.” Wilentz and his fellow signatories didn’t just dispute the Times Magazine’s interpretation of past events, but demanded corrections.

Underlying each of the disagreements in the letter is not just a matter of historical fact but a conflict about whether Americans, from the Founders to the present day, are committed to the ideals they claim to revere.

while some of the critiques can be answered with historical fact, others are questions of interpretation grounded in perspective and experience.

Both sides agree, as many of the project’s right-wing critics do not, that slavery’s legacy still shapes American life—an argument that is less radical than it may appear at first glance. If you think anti-black racism still shapes American society, then you are in agreement with the thrust of the 1619 Project, though not necessarily with all of its individual arguments.

Was America founded as a slavocracy, and are current racial inequities the natural outgrowth of that? Or was America conceived in liberty, a nation haltingly redeeming itself through its founding principles?

The clash between the Times authors and their historian critics represents a fundamental disagreement over the trajectory of American society

The 1619 Project, and Hannah-Jones’s introductory essay in particular, offer a darker vision of the nation, in which Americans have made less progress than they think, and in which black people continue to struggle indefinitely for rights they may never fully realize.

The letter is rooted in a vision of American history as a slow, uncertain march toward a more perfect union.

Inherent in that vision is a kind of pessimism, not about black struggle but about the sincerity and viability of white anti-racism.

“The biggest obstacle to teaching slavery effectively in America is the deep, abiding American need to conceive of and understand our history as ‘progress,’ as the story of a people and a nation that always sought the improvement of mankind, the advancement of liberty and justice, the broadening of pursuits of happiness for all,”

The letter’s signatories recognize the problem the Times aimed to remedy, Wilentz told me. “Each of us, all of us, think that the idea of the 1619 Project is fantastic. I mean, it's just urgently needed. The idea of bringing to light not only scholarship but all sorts of things that have to do with the centrality of slavery and of racism to American history is a wonderful idea,” he said. In a subsequent interview, he said, “Far from an attempt to discredit the 1619 Project, our letter is intended to help it.”

The letter disputes a passage in Hannah-Jones’s introductory essay, which lauds the contributions of black people to making America a full democracy and says that “one of the primary reasons the colonists decided to declare their independence from Britain was because they wanted to protect the institution of slavery” as abolitionist sentiment began rising in Britain.

“To teach children that the American Revolution was fought in part to secure slavery would be giving a fundamental misunderstanding not only of what the American Revolution was all about but what America stood for and has stood for since the Founding,” Wilentz told me. Anti-slavery ideology was a “very new thing in the world in the 18th century,” he said, and “there was more anti-slavery activity in the colonies than in Britain.”

Where Wilentz and his colleagues see the rising anti-slavery movement in the colonies and its influence on the Revolution as a radical break from millennia in which human slavery was accepted around the world, Hannah-Jones’ essay outlines how the ideology of white supremacy that sustained slavery still endures today.

On this question, the critics of the 1619 Project are on firm ground. Although some southern slave owners likely were fighting the British to preserve slavery, as Silverstein writes in his rebuttal, the Revolution was kindled in New England, where prewar anti-slavery sentiment was strongest. Early patriots like James Otis, John Adams, and Thomas Paine were opposed to slavery, and the Revolution helped fuel abolitionism in the North.

it is this profound pessimism about white America that many of the 1619 Project’s critics find most galling.

The most radical thread in the 1619 Project is not its contention that slavery’s legacy continues to shape American institutions; it’s the authors’ pessimism that a majority of white people will abandon racism and work with black Americans toward a more perfect union.

In an interview with the World Socialist Web Site, Oakes said, “The function of those tropes is to deny change over time … The worst thing about it is that it leads to political paralysis. It’s always been here. There’s nothing we can do to get out of it. If it’s the DNA, there’s nothing you can do. What do you do? Alter your DNA?”

The project’s pessimism has drawn criticism from the left as well as the right.

A major theme of the 1619 Project is that the progress that has been made has been fragile and reversible—and has been achieved in spite of the nation’s true founding principles, which are not the lofty ideals few Americans genuinely believe in.

“The fight for black freedom is a universal fight; it's a fight for everyone. In the end, it affected the fight for women's rights—everything. That's the glory of it,” Wilentz told me. “To minimize that in any way is, I think, bad for understanding the radical tradition in America.”

the question of whether black Americans have fought their freedom struggles “largely alone,” as Hannah-Jones put it in her essay, is subject to vigorous debate.

“I think one would be very hard-pressed to look at the factual record from 1619 to the present of the black freedom movement and come away with any conclusion other than that most of the time, black people did not have a lot of allies in that movement,” Hannah-Jones told me. “It is not saying that black people only fought alone. It is saying that most of the time we did.”

“The tone to me rather suggested a deep-seated concern about the project. And by that I mean the version of history the project offered. The deep-seated concern is that placing the enslavement of black people and white supremacy at the forefront of a project somehow diminishes American history,”

To Wilentz, the failures of earlier scholarship don’t illustrate the danger of a monochromatic group of historians writing about the American past, but rather the risk that ideologues can hijack the narrative. “[It was] when the southern racists took over the historical profession that things changed, and W. E. B. Du Bois fought a very, very courageous fight against all of that,” Wilentz told me. The Dunning School, he said, was “not a white point of view; it’s a southern, racist point of view.”

In the letter, Wilentz portrays the authors of the 1619 Project as ideologues as well. He implies—apparently based on a combative but ambiguous exchange between Hannah-Jones and the writer Wesley Yang on Twitter—that she had discounted objections raised by “white historians” since publication.

Hannah-Jones told me she was misinterpreted. “I rely heavily on the scholarship of historians no matter what race, and I would never discount the work of any historian because that person is white or any other race,” she told me. “I did respond to someone who was saying white scholars were afraid, and I think my point was that history is not objective. And that people who write history are not simply objective arbiters of facts, and that white scholars are no more objective than any other scholars, and that they can object to the framing and we can object to their framing as well.”

When I asked Wilentz about Hannah-Jones’s clarification, he was dismissive. “Fact and objectivity are the foundation of both honest journalism and honest history. And so to dismiss it, to say, ‘No, I'm not really talking about whites’—well, she did, and then she takes it back in those tweets and then says it's about the inability of anybody to write objective history. That's objectionable too,”

The problem, as Du Bois argued, is that much of American history has been written by scholars offering ideological claims in place of rigorous historical analysis. But which claims are ideological, and which ones are objective, is not always easy to discern.

Newt Gingrich called the 1619 Project a “lie,” arguing that “there were several hundred thousand white Americans who died in the Civil War in order to free the slaves." In City Journal, the historian Allen Guelzo dismissed the Times Magazine project as a “conspiracy theory” developed from the “chair of ultimate cultural privilege in America, because in no human society has an enslaved people suddenly found itself vaulted into positions of such privilege, and with the consent—even the approbation—of those who were once the enslavers.

They are the chosen people who bent history to their will by holding up their visions of the future.

The Crown’s portrayal of Thatcher evokes a form of nostalgia for the certainty of the past that she has come to represent. With the benefit of hindsight, we can see that on some big calls, she was right: on remaking Britain’s moribund economy, for example, and retaking the Falkland Islands

Today we see her as a leader who saw what needed to be done to get to where she wanted to go. And in one sense that designation is evidently true. Thatcher was a political titan of iron will and intellectual vigor who did change Britain—for good or ill, depending on your view.

Wouldn’t the country, like so many of its neighbors, have eventually grasped its way to some kind of economic reform and ended up, roughly, where it is today?

Does the story we tell about Thatcher, then, not reveal more about us than it does about her? Is the point, in fact, that we need the myth of Thatcher—the visionary and transformational leader—to affirm to ourselves that we too can make a difference and change the world

Obama’s memoir seems to grapple with this inconvenient problem, but the former president cannot stop believing in his own myth. How does he explain Donald Trump’s election, for example? In his interview with The Atlantic’s Jeffrey Goldberg, Obama says Trump’s rise is partly a reaction to his own success, and partly the consequence of a changing media landscape

In other words, Trump’s election does not undermine Obama’s victories or vision, because circumstances beyond his control subsequently changed for the worse.

It was not, fundamentally, because of anything Obama had done wrong, or any of his own character flaws. Crucially, it was also not because his promise of a better America was wrong.

The argument that policy failures, character flaws, personal weakness, or legitimate public distaste was the real reason leaders or their philosophies were rejected is rarely countenanced.

it is possible to discern something of an iron rule for former political leaders: Nothing can ever happen after power has been relinquished that in any fundamental way proves their central political analysis wrong

Politicians have long understood that their ability to forecast the future—to be on the right side of history—is central to their legitimacy as decision makers

a leader leads by anticipating the future using their understanding of how the past led to the present. For any statesman to admit that he failed to foresee the future is to admit that he failed as a statesman.

Obama—like almost all political leaders—feels vindicated by events, even as they drift further and further away from the path he foresaw.

“I’m convinced that the pandemic we’re currently living through is both a manifestation of and a mere interruption in the relentless march toward an interconnected world,”

they must persist in arguing that however far from the path the world has veered since their departure, the destination remains the same: that the arc bends just as they prophesied.

Our leaders must project perfection and certainty. We need them to in order to make us relevant.

National Greatness Conservatism. We argued that the G.O.P. had become too anti-government. “How can Americans love their nation if they hate its government?” we asked. Only a return to the robust American nationalism of Alexander Hamilton, Henry Clay and Theodore Roosevelt would do: ambitious national projects, infrastructure, federal programs to increase social mobility.

George W. Bush, who made his own leap, to Compassionate Conservatism. (You know somebody has made a paradigm leap when he or she starts adding some modifying word or phrase before “Conservatism.”) This was an attempt to meld Catholic social teaching to conservatism.

am’s Club Republicans, led by Reihan Salam and my Times colleague Ross Douthat, pointed a way to link the G.O.P. to working-class concerns

Front Porch Republicans celebrated small towns and local communities.

The Reformicons tried to use government to build strong families and neighborhoods

The Niskanen Center is an entire think tank for people who have leapt from libertarianism.

Most actual Republican politicians rejected all of this. They stuck, mostly through dumb inertia, to an anti-government zombie Reaganism long after Reagan was dead and even though the nation’s problems were utterly different from what they were when he was alive

Donald Trump and Bannon took a low-rent strand of conservatism — class-based ethnic nationalism — that had always been locked away in the basement of the American right, and overturned the Reagan paradigm.

Bannon and Trump got the emotions right. They understood that Republican voters were no longer motivated by a sense of hope and opportunity; they were motivated by a sense of menace, resentment and fear. At base, many Republicans felt they were being purged from their own country — by the educated elite, by multiculturalism, by militant secularism.

During the 2016 presidential campaign, Trump and Bannon discarded the Republican orthodoxy — entitlement reform, fiscal restraint, free trade, comprehensive immigration reform. They embraced a European-style blood-and-soil conservatism. Close off immigration. Close trade. We have nothing to offer the world and should protect ourselves from its dangers.

Over the last three years, it’s been interesting to watch a series of Republican officeholders break free from old orthodoxies and begin to think afresh.

future is embodied by a small group of Republican senators in their 40s, including Marco Rubio, Josh Hawley, Tom Cotton and Ben Sasse.

they start with certain common Trumpian premises:

Everything is not OK. The free market is not working well. Wages are stagnant. Too much power is in the hands of the corporate elites. Middle America is getting screwed. Finance capitalism is unbalanced. American society is in abject decline. If Reaganism was “Let’s be free,” the new mood is “Take control.

Economic libertarianism is not the answer. Free markets alone won’t solve our problems

We need policies to shore up the conservative units of society — family, neighborhood, faith, nation. We need policies that build solidarity, not just liberty.

The working class is the heart of the Republican Party. Once, businesspeople and entrepreneurs were at the center of the Republican imagination. Now it’s clear that the party needs to stop catering to the corporate class and start focusing on the shop owners, the plumbers, the salaried workers

China changes everything. The rise of a 1.4-billion-person authoritarian superpower means that free trade no longer works because the Chinese are not playing by the same rules

The managerial class betrays America. Many of the post-Reagan positions seem like steps to the left. But these Republicans combine a greater willingness to use government with a greater hostility to the managerial class.

From these common premises the four senators go off in different directions.

Hawley is the most populist of the group. His core belief is that middle-class Americans have been betrayed by elites on every level — political elites, cultural elites, financial elites

The modern leadership class has one set of values — globalization, cosmopolitanism — and the Middle Americans have another set — family, home, rootedness, nation

Cotton has a less developed political vision but a more developed attitude: hawkishness.

Sasse is the most sociological of the crew. He is a Tocquevillian localist, who notes that most normal Americans go days without thinking of national politics. His vision is centered on the small associations — neighborhood groups, high school football teams, churches and community centers — where people find their greatest joys, satisfactions and supports.

over the long term, some version of Working-Class Republicanism will redefine the G.O.P. In the first place, that’s where Republican voters are

Government’s job, he says, is to “create a framework of ordered liberty” so that people can make their family and neighborhood the center of their lives.

Levin thinks the prevailing post-Trump viewpoints define the problem too much in economic terms. The crucial problem, he argues, is not economic; it’s social: alienation. Millions of Americans don’t feel part of anything they can trust. They feel no one is looking out for them.

“What’s needed,” Levin says, “is not just to expand economic conservatism beyond growth to also prioritize family, community and nation, but also to expand social conservatism beyond sexual ethics and religious liberty to prioritize family, community and nation

The Republican Party looks completely brain-dead at every spot Trump directly reaches. Off in the corners, though, there’s a lot of intellectual ferment on the right.

The Wall Street Journal editorial page stands as a vigilant guardian of the corpse, eager to rebut all dissenters. The former U.N. ambassador Nikki Haley and Senator Pat Toomey of Pennsylvania are staunch defenders of Minimal-Government Conservatism

there’s also the possibility that Republicans will abandon any positive vision and revert to being a simple anti-government party — a party of opposition to whatever Biden is doing.

Behind these public figures there is a posse of policy wonks and commentators supporting a new Working-Class Republicanism,

Second, the working-class emphasis is the only way out of the demographic doom loop. If the party sticks with its old white high school-educated base, it will die. They just aren’t making enough old white men.

None of this works unless Republicans can deracialize their appeal — by which I mean they must stop pandering to the racists in the party and stop presenting themselves and seeing themselves as the party of white people

The competing vision—let’s call it race-consciousness—was best encapsulated by the Black Power movement

it was to demand that black people, understood as a collective, receive more recognition, more respect, and more resources. Underlying this vision was the assumption that society is a zero-sum power struggle between oppressed groups and oppressor groups—and that a win for the former requires a loss for the latter.

In the race-conscious vision, racial harmony is an afterthought. At times, it is actively shunned. Race-consciousness seeks to “problematize” relations between members of different ethnic groups in a variety of ways

For black people, race-consciousness seems to promise more status and more access to opportunity. For white people, it promises a way to act on, rather than simply brood over, feelings of guilt over their complicity (real or imagined) in America’s past sins. For the nation as a whole, it seems to promise solutions to ongoing problems like mass incarceration and police brutality.

Yet race-consciousness cannot deliver on its promises because its foundational assumptions are flawed. For one thing, it does not reject the old rigid racial categories so much as it transforms them, sneaking them in through the back door.

More fundamentally, race-consciousness misdiagnoses the problems facing our society and therefore prescribes the wrong cures. The preoccupation with electing black politicians (or politicians “of color” more broadly) is one example.

Cities such as Atlanta and Detroit, which have had five or six consecutive black mayors, see all the same problems as cities with mostly white leadership. As Bernie Sanders pointed out not long ago, caring about the skin color of politicians, as opposed to their policy proposals and qualifications, is just as wrong-headed as it sounds.

Where will race-conscious anti-racism of this kind lead in the long run?

We might get a clue from the work put forth by thought leaders within the movement. Consider Ibram X. Kendi, the bestselling author of How to Be an Antiracist, who has proposed a constitutional amendment that would enable actual authoritarianism. I do not use that word lightly: In Kendi’s ideal world, there would be a Department of Anti-Racism that would have the constitutional power to investigate private businesses, reject any local, state, or federal policy that is deemed to contribute to racial disparity, and discipline public officials “who do not voluntarily change their racist policy and ideas.” (What counts as a racist idea would, of course, be determined by a panel of experts like Kendi.)

Thankfully, very few people would sign on to Kendi’s proposal right now. But it is still a useful document for one reason: it accurately summarizes what would be required in order to achieve the world that today’s race-conscious anti-racists want to see

A movement that defines any racially disparate outcome as white supremacy will inevitably tend toward policies that seek to erase such disparities by fiat—individual rights be damned. If, for instance, the fact that Asian-Americans are vastly over-represented in New York City’s elite high schools (which admit students on the basis of a single test) comes to be seen as a racist outcome, then Asian-American applicants may be discriminated against to eliminate that disparity.

The question is not whether a proposal like Kendi’s could gain enough support to be implemented wholesale today; it couldn’t.

The question is this: if Kendi’s proposal enters the political mainstream in, say, fifty years, will there be a robust, liberal anti-racist movement to provide an alternative? Or will liberal principles—such as individual rights and freedom of speech—have been so thoroughly stigmatized that Kendi-like proposals seem to be the only viable option for those who care about fighting racism?

Writers such as Ibram X. Kendi and Robin DiAngelo have done an excellent job of owning the term “anti-racist.”

Many people who are horrified by their illiberalism are thus tempted to give up on the label of anti-racism. That would be a mistake—for it is up to us whether anti-racism will continue to move in an illiberal direction

America has a long tradition of liberal anti-racism that reaches back to Martin Luther King, A. Philip Randolph, Bayard Rustin, Frederick Douglass, and beyond. It is an anti-racism grounded in the idea that there is a single human race to which we all belong

Today, many feel that this principle represents the very status quo that we must depart from in order to begin making progress. The goal of getting past race, in this view, is precisely what has prevented us from implementing the race-conscious policies that would meaningfully address racial inequality.

But this underplays how much progress we have already made. Back in the early 1970s, the NYPD killed 91 people in a single year. In 2018, they killed five. Since 2001, the national incarceration rate for black men ages 18-29 has been cut by more than half. Most people don’t know this

As a result, they imagine that the system must be overturned in order for progress to occur. But though there are, of course, still a lot of injustices in today’s America, they are wrong.

The current system, warts and all, has enabled huge progress for black people in recent decades. Overturning the liberal principles on which our institutions are based would not hasten progress towards racial equality; it would threaten the very stability that is required for incremental progress to occur.

It is time to restore Martin Luther King’s dream for American race relations—a dream that, even as it refuses to flinch from the injustices we still need to overcome, defiantly holds onto the idea that what we have in common is ultimately more important than what divides us. We must defend that principle even when it is unpopular, even when it marks you as “tone-deaf,” and even when it elicits eyerolls from those who imagine they have found more worthy principles. Our ability to remedy racial injustice depends on it.

conservative activists and business leaders designed an opposite idea of American freedom to protect their own interests

Realtors had big incentives for maintaining segregation. Having invented it in the early 1900s as a marketing tool for selling homes, they had made segregation central to their business practices. They created racial covenants to exclude members of minority groups from new developments, existing neighborhoods, and entire cities and shaped federal redlining maps, all premised on the idea that anyone selling to minority families was destroying the future of all the neighbors.

Despite the Supreme Court outlawing court enforcement of racial covenants in 1948, Realtors used racial steering—such as lying to minority prospective buyers that a home had just been sold and controlling newspaper real-estate listings—so effectively that by the early ’60s, Black Americans were excluded from 98 percent of new homes and 95 percent of neighborhoods.

in asking voters to constitutionally authorize residential discrimination in Proposition 14, Realtors had a fundamental problem. How, at the height of the civil-rights movement, could they publicly campaign for sanctioning discrimination in California?

Victory would depend, realized Spike Wilson, the president of the California Real Estate Association, on convincing the large majority of white voters—who did not want to see themselves as racially prejudiced in any way—that the Realtors were campaigning not for discrimination but for American freedom.

Realtors would need to secretly and systematically redefine American freedom as the freedom to discriminate—to challenge the idea at the heart of the civil-rights movement itself.

the national Realtors’ organization created a secret action kit to oppose fair housing everywhere.

The kit’s detailed scripts instructed Realtors to “focus on freedom” and avoid “discussion of emotionally charged subjects,” such as “inferiority of races.”

Freedom, the kit explained, meant each owner’s right to discriminate, and Realtors were in favor of “freedom for all”: the equal rights of all owners to choose whom to sell to. Realtors claimed that they, unlike civil-rights advocates, were color-blind.

Wilson drafted a Property Owners’ Bill of Rights that Realtors advertised in newspapers nationwide, emphasizing owners’ absolute right to dispose of their property—never mentioning anyone’s right to buy or rent a home in the first place

This was not always the case. In the early 1960s, civil-rights activists invoked freedom as the purpose of their struggle. Martin Luther King Jr. used the word equality once at the March on Washington, but he used the word freedom 20 times.

Realtors thus made government the enemy, not minority groups

Thus, the more disparate the issues on which this idea of freedom was invoked—abortion, guns, public schools, gender rights, campaign finance, climate change—the more powerful the message became.

By making state bureaucrats the enemy, Realtors could be on the side of the underdog, the individual owner. Proposition 14, Realtors claimed, was not about race but about “the rights of the individual.”

To discriminate simply means to choose, Realtors insisted. Freedom of choice required the right to discriminate.

To be in favor of Proposition 14, to limit where millions of fellow Americans could live, did not mean that you were prejudiced but that you believed in individual freedom.

Wilson cited Abraham Lincoln: “We are involved in a great battle for liberty and freedom. We have prepared a final resting place for the drive to destroy individual freedom.”

King’s terms evoked his speech at the March on Washington, but he was now defending shared freedom not against southern diehards but against northern salesmen promoting color-blind “freedom of choice.”

Proposition 14’s sweeping passage stunned politicians in both parties. The Realtors’ victory was overwhelming, with 65 percent of the total votes in favor, including 75 percent of the white vote and 80 percent of the white union vote.

Color-blind freedom meant that government must be oblivious to, must forever allow, organized private discrimination.

Reagan and other conservatives saw that the Realtors had zeroed in on something extremely powerful—something whose full force would not be limited to housing segregation but could be used on virtually any issue.

Realtors had shown how conservatives could succeed. If this idea of freedom could triumph in California, it could work anywhere.

though Realtors have disavowed their past arguments, the vision of freedom they created has had lasting effects on American politics as a whole.

This vision of freedom proved so enduring because it solved three structural problems for American conservatism.

First, Realtors used the language of individual freedom, of libertarianism, to justify its seeming opposite, community conformity.

Here was a way to unite the two separate and competing strands of conservatism, to link libertarians and social conservatives in defense of American freedom—and create the way many, if not most, Americans understand freedom today.

Reagan, running for governor, adopted the Realtors’ cause and their message as his own: “If an individual wants to discriminate against Negroes or others in selling or renting his house, he has a right to do so.”

a unifying idea: freedom of choice.

Second, by defining as freedom what government seemed to be taking away from “ordinary Americans,” Realtors helped create a polarizing, transcendent view of what was at stake in our politics

This picture of government taking away your rights would provide a compelling reason, far beyond economics, for millions of union members, Catholics, and white Americans who had long been part of Franklin D. Roosevelt’s coalition to see, in issue after issue, why they should define themselves as conservatives.

Timeliest of all, the Realtors’ redefinition of freedom offered a common ideology for something new in modern America: a national conservative political party

The Realtors’ color-blind freedom, which had proved so successful in California, could unite southerners, working-class northern Democrats, and conservative and moderate Republicans in a new national majority party—one very different from the Republican Party whose congressmen had voted 80 percent in favor of the Civil Rights and Voting Rights Acts.

Over time, the internal dynamics of a national conservative party would only push it further and further toward those who most ardently embraced the Realtors’ vision of freedom as the only meaning of American freedom. This dynamic has produced today’s Republican Party.

Republican politicians now view every issue through this single lens: that American freedom means placing one’s own absolute rights over those of others.

To go against that credo, to view freedom as belonging to the country itself and, as such, to everyone equally, threatens the party’s most basic tenet.

This idea of freedom is based on a technique that the Realtors perfected. They identified a single, narrow, obscure right, an owner’s right to choose a buyer—which Realtors themselves had restricted for decades with racial covenants—as American freedom itself.

Elevating as absolute a right rarely mentioned before, so government cannot limit it or protect the rights of others, became the model for the conservative movement

The concept can be and has been used regarding virtually any issue.

Everything that is not one of these carefully selected rights becomes, by definition, a privilege that government cannot protect, no matter how fundamental.

Since January 6, two-thirds of Republicans—more than 40 percent of all Americans—now see voting not as a basic right, an essential part of our freedom, but as a privilege for those who deserve it.

This picture of freedom has a purpose: to effectively prioritize the freedoms of certain Americans over the freedoms of others—without directly saying so

To seek to do it on the basis that Labour can again be a national party in geographical and class terms, winning working- and middle class support alike, deserves that description even more.

It challenges the view that the dominant progressive parties of the industrial era must accommodate themselves to operating within a more pluralist party system and amid the looser class loyalties of the new millennium

it says that such segmentation is neither inevitable nor even desirable, providing that the party remains a broad church and – crucially – avoids foolish accommodations with the activist left.

it is certainly not New Labour 2.0 either, and calling it so does not make it so. Indeed the Starmer strategy of focusing on working-class support is at odds with one of New Labour’s most central tenets.

Tony Blair and Gordon Brown believed that Labour would prosper in the modern era only by reducing its dependence on working-class voters and the unions and by becoming a middle-class, progressive party, like the US Democrats.

This is actually quite a traditional, and almost old-fashioned, view of Labour’s role. Starmer’s aspiration to make Labour a national and essentially social democratic party again is one that Clement Attlee or Harold Wilson would have understood.

Degrowth

notice I said the word “some”. Many progressive visions, like greater economic equality, the closing of racial wealth gaps, and the reversal of climate change, are things I want!

what I’m arguing is that some of the big ideas progressives embraced in the heady rush of the 2010s are misguided and should be discarded, in order to work toward utopias that human beings would actually like to live in.

Here’s a list of four such visions.

When Brad challenged me to list some examples of dystopian progressive visions, I immediately said “degrowth”, and he agreed.

halting or reversing economic growth — an idea that has become fashionable among some progressive circles in the past decade — is both unworkable and undesirable as a way to limit humanity’s environmental impact

First, I argued that the drop in living standards that degrowth would require makes it a political nonstarter, and the amount of global central planning involved would be impossible to implement:

I also argued that solving climate change requires growth, since it’ll take a lot of economic output to replace our energy sources with solar and wind and batteries. And then once we do switch to those energy sources, they’ll be so cheap (thanks to learning curves) that we’ll actually have sustainably higher consumption than before.

As I explained in that second post, I view degrowth partly as an attempt to valorize national decline, which is why the idea is much more popular in Europe than in the U.S.

The expulsion of “colonizers”

ome progressives in the U.S. have begun to talk about an entirely different type of “decolonization” — the expulsion of “settler colonial” populations from regions that their ancestors settled in.

the real problem with Mr. Andreessen’s manifesto may be not that it’s too outlandish, but that it’s too on-the-nose.

Neoreactionary thought contends that the world would operate much better in the hands of a few tech-savvy elites in a quasi-feudal system. Mr. Andreessen, through this lens, believes that advancing technology is the most virtuous thing one can do.

And the way we regard that wealth as a product of good decision-making and righteous hard work, no matter how many billions of dollars of investors’ money they may have vaporized, how many other people contributed to their success or how much government money subsidized it

In the case of ordinary individuals, however, debt is regarded as not just a financial failure but a moral one. (If you are successful and have paid your student loans off, taking them out in the first place was a good decision. If you haven’t and can’t, you were irresponsible and the government should not enable your freeloading.)

He articulates (albeit in a refrigerator magnet poetry kind of way) a strain of nihilism that has gained traction among tech elites, and reveals much of how they think about their few remaining responsibilities to society.

This strain of thinking is disdainful of democracy and opposes institutions (a free press, for example) that bolster it. It despises egalitarianism and views oppression of marginalized groups as a problem of their own making.

Who is doing the telling here, and who is being told? It’s not technology (a term so broad it encompasses almost everything) that’s reducing wages and increasing inequality — it’s the ultrawealthy, people like Mr. Andreessen.

It argues for an extreme acceleration of technological advancement regardless of consequences, in a way that makes “move fast and break things” seem modest.

Mr. Andreessen claims to be against authoritarianism, but really, it’s a matter of choosing the authoritarian — and the neoreactionary authoritarian of choice is a C.E.O. who operates as king.

it is taken seriously by people who imagine themselves potential Chief Executive Authoritarians, or at the very least proxies. This includes another Silicon Valley billionaire, Peter Thiel, who has funded some of Mr. Yarvin’s work and once wrote that he believed democracy and freedom were incompatible.

how did they sell so many other people on it? By pretending that for all their wealth and influence, they are not the real elites.

When Mr. Andreessen says “we” are being lied to, he includes himself, and when he names the liars, they’re those in “the ivory tower, the know-it-all credentialed expert worldview,” who are “disconnected from the real world, delusional, unelected, and unaccountable — playing God with everyone else’s lives, with total insulation from the consequences.”

His depiction of academics of course sounds a lot like — well, like tech overlords, who are often insulated from the real-world consequences of their inventions, including but not limited to promoting disinformation, facilitating fraud and enabling genocidal regimes.

“We are told that technology takes our jobs,” Mr. Andreessen writes, “reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.”

You can see it in the way we valorize the C.E.O.s of “unicorn” companies who have expanded their wealth far beyond what could possibly be justified by their individual contributions

The argument for total acceleration of technological development is not about optimism, except in the sense that the Andreessens and Thiels and Musks are certain that they will succeed. It’s pessimism about democracy — and ultimately, humanity.

the billionaire classes of Silicon Valley are frustrated that they cannot just accelerate their way into the future, one in which they can become human/technological hybrids and live forever in a colony on Mars

In pursuit of this accelerated post-Singularity future, any harm they’ve done to the planet or to other people is necessary collateral damage. It’s the delusion of people who’ve been able to buy their way out of everything uncomfortable, inconvenient or painful, and don’t accept the fact that they cannot buy their way out of death.

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