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“The curriculum covers topics from causal modeling and probability to game theory and cognitive science,” read a website promising teens a summer of Rationalist learning. “How can we understand our own reasoning, behavior, and emotions? How can we think more clearly and better achieve our goals?”

Some lived in group houses. Some practiced polyamory. “They are basically just hippies who talk a lot more about Bayes’ theorem than the original hippies,” said Scott Aaronson, a University of Texas professor who has stayed in one of the group houses.

For Kelsey Piper, who embraced these ideas in high school, around 2010, the movement was about learning “how to do good in a world that changes very rapidly.”

Yes, the community thought about A.I., she said, but it also thought about reducing the price of health care and slowing the spread of disease.

Slate Star Codex, which sprung up in 2013, helped her develop a “calibrated trust” in the medical system. Many people she knew, she said, felt duped by psychiatrists, for example, who they felt weren’t clear about the costs and benefits of certain treatment.

That was not the Rationalist way.

“There is something really appealing about somebody explaining where a lot of those ideas are coming from and what a lot of the questions are,” she said.

Sam Altman, chief executive of OpenAI, an artificial intelligence lab backed by a billion dollars from Microsoft. He was effusive in his praise of the blog.It was, he said, essential reading among “the people inventing the future” in the tech industry.

Mr. Altman, who had risen to prominence as the president of the start-up accelerator Y Combinator, moved on to other subjects before hanging up. But he called back. He wanted to talk about an essay that appeared on the blog in 2014.The essay was a critique of what Mr. Siskind, writing as Scott Alexander, described as “the Blue Tribe.” In his telling, these were the people at the liberal end of the political spectrum whose characteristics included “supporting gay rights” and “getting conspicuously upset about sexists and bigots.”

But as the man behind Slate Star Codex saw it, there was one group the Blue Tribe could not tolerate: anyone who did not agree with the Blue Tribe. “Doesn’t sound quite so noble now, does it?” he wrote.

Mr. Altman thought the essay nailed a big problem: In the face of the “internet mob” that guarded against sexism and racism, entrepreneurs had less room to explore new ideas. Many of their ideas, such as intelligence augmentation and genetic engineering, ran afoul of the Blue Tribe.

Mr. Siskind was not a member of the Blue Tribe. He was not a voice from the conservative Red Tribe (“opposing gay marriage,” “getting conspicuously upset about terrorists and commies”). He identified with something called the Grey Tribe — as did many in Silicon Valley.

The Grey Tribe was characterized by libertarian beliefs, atheism, “vague annoyance that the question of gay rights even comes up,” and “reading lots of blogs,” he wrote. Most significantly, it believed in absolute free speech.

The essay on these tribes, Mr. Altman told me, was an inflection point for Silicon Valley. “It was a moment that people talked about a lot, lot, lot,” he said.

And in some ways, two of the world’s prominent A.I. labs — organizations that are tackling some of the tech industry’s most ambitious and potentially powerful projects — grew out of the Rationalist movement.

In 2005, Peter Thiel, the co-founder of PayPal and an early investor in Facebook, befriended Mr. Yudkowsky and gave money to MIRI. In 2010, at Mr. Thiel’s San Francisco townhouse, Mr. Yudkowsky introduced him to a pair of young researchers named Shane Legg and Demis Hassabis. That fall, with an investment from Mr. Thiel’s firm, the two created an A.I. lab called DeepMind.

Like the Rationalists, they believed that A.I could end up turning against humanity, and because they held this belief, they felt they were among the only ones who were prepared to build it in a safe way.

In 2014, Google bought DeepMind for $650 million. The next year, Elon Musk — who also worried A.I. could destroy the world and met his partner, Grimes, because they shared an interest in a Rationalist thought experiment — founded OpenAI as a DeepMind competitor. Both labs hired from the Rationalist community.

Mr. Aaronson, the University of Texas professor, was turned off by the more rigid and contrarian beliefs of the Rationalists, but he is one of the blog’s biggest champions and deeply admired that it didn’t avoid live-wire topics.

“It must have taken incredible guts for Scott to express his thoughts, misgivings and questions about some major ideological pillars of the modern world so openly, even if protected by a quasi-pseudonym,” he said

In late June of last year, not long after talking to Mr. Altman, the OpenAI chief executive, I approached the writer known as Scott Alexander, hoping to get his views on the Rationalist way and its effect on Silicon Valley. That was when the blog vanished.

The issue, it was clear to me, was that I told him I could not guarantee him the anonymity he’d been writing with. In fact, his real name was easy to find because people had shared it online for years and he had used it on a piece he’d written for a scientific journal. I did a Google search for Scott Alexander and one of the first results I saw in the auto-complete list was Scott Alexander Siskind.

More than 7,500 people signed a petition urging The Times not to publish his name, including many prominent figures in the tech industry. “Putting his full name in The Times,” the petitioners said, “would meaningfully damage public discourse, by discouraging private citizens from sharing their thoughts in blog form.” On the internet, many in Silicon Valley believe, everyone has the right not only to say what they want but to say it anonymously.

I spoke with Manoel Horta Ribeiro, a computer science researcher who explores social networks at the Swiss Federal Institute of Technology in Lausanne. He was worried that Slate Star Codex, like other communities, was allowing extremist views to trickle into the influential tech world. “A community like this gives voice to fringe groups,” he said. “It gives a platform to people who hold more extreme views.”

I assured her my goal was to report on the blog, and the Rationalists, with rigor and fairness. But she felt that discussing both critics and supporters could be unfair. What I needed to do, she said, was somehow prove statistically which side was right.

When I asked Mr. Altman if the conversation on sites like Slate Star Codex could push people toward toxic beliefs, he said he held “some empathy” for these concerns. But, he added, “people need a forum to debate ideas.”

In August, Mr. Siskind restored his old blog posts to the internet. And two weeks ago, he relaunched his blog on Substack, a company with ties to both Andreessen Horowitz and Y Combinator. He gave the blog a new title: Astral Codex Ten. He hinted that Substack paid him $250,000 for a year on the platform. And he indicated the company would give him all the protection he needed.

In the modern tech world, the value of a humanistic education shows up in evidence of its absence. Sam Bankman-Fried, the disgraced founder of the crypto exchange FTX who recently lost his $16 billion fortune in a few days, is a famously proud illiterate. “I would never read a book,” he once told an interviewer. “I don’t want to say no book is ever worth reading, but I actually do believe something pretty close to that.”

Elon Musk and Twitter are another excellent case in point. It’s painful and extraordinary to watch the ham-fisted way a brilliant engineering mind like Musk deals with even relatively simple literary concepts such as parody and satire. He obviously has never thought about them before.

The extraordinary ignorance on questions of society and history displayed by the men and women reshaping society and history has been the defining feature of the social-media era. Apparently, Mark Zuckerberg has read a great deal about Caesar Augustus, but I wish he’d read about the regulation of the pamphlet press in 17th-century Europe. It might have spared America the annihilation of social trust.

These failures don’t derive from mean-spiritedness or even greed, but from a willful obliviousness. The engineers do not recognize that humanistic questions—like, say, hermeneutics or the historical contingency of freedom of speech or the genealogy of morality—are real questions with real consequences

Everybody is entitled to their opinion about politics and culture, it’s true, but an opinion is different from a grounded understanding. The most direct path to catastrophe is to treat complex problems as if they’re obvious to everyone. You can lose billions of dollars pretty quickly that way.

As the technologists have ignored humanistic questions to their peril, the humanists have greeted the technological revolutions of the past 50 years by committing soft suicide.

As of 2017, the number of English majors had nearly halved since the 1990s. History enrollments have declined by 45 percent since 2007 alone

the humanities have not fundamentally changed their approach in decades, despite technology altering the entire world around them. They are still exploding meta-narratives like it’s 1979, an exercise in self-defeat.

Contemporary academia engages, more or less permanently, in self-critique on any and every front it can imagine.

the situation requires humanists to explain why they matter, not constantly undermine their own intellectual foundations.

The humanities promise students a journey to an irrelevant, self-consuming future; then they wonder why their enrollments are collapsing. Is it any surprise that nearly half of humanities graduates regret their choice of major?

Despite the clear value of a humanistic education, its decline continues. Over the past 10 years, STEM has triumphed, and the humanities have collapsed. The number of students enrolled in computer science is now nearly the same as the number of students enrolled in all of the humanities combined.

now there’s GPT-3. Natural-language processing presents the academic humanities with a whole series of unprecedented problems

Practical matters are at stake: Humanities departments judge their undergraduate students on the basis of their essays. They give Ph.D.s on the basis of a dissertation’s composition. What happens when both processes can be significantly automated?

despite the drastic divide of the moment, natural-language processing is going to force engineers and humanists together. They are going to need each other despite everything. Computer scientists will require basic, systematic education in general humanism: The philosophy of language, sociology, history, and ethics are not amusing questions of theoretical speculation anymore. They will be essential in determining the ethical and creative use of chatbots, to take only an obvious example.

The humanists will need to understand natural-language processing because it’s the future of language

that space for collaboration can exist, both sides will have to take the most difficult leaps for highly educated people: Understand that they need the other side, and admit their basic ignorance.

But that’s always been the beginning of wisdom, no matter what technological era we happen to inhabit.

The political and methodological stresses within the historical profession are intensified by economic troubles. For a long time, but especially since the economic crisis of 2008, university students have turned away from the humanities, preferring to major in fields that seem to offer more certain and lucrative employment. Consequently, academic jobs in the humanities and especially in history have become radically more precarious for younger faculty—even as universities have sought to meet diversity goals in their next-generation hiring by expanding offerings in history-adjacent specialties, such as gender and ethnic studies.

The result has produced a generational divide. Younger scholars feel oppressed and exploited by universities pressing them to do more labor for worse pay with less security than their elders; older scholars feel that overeager juniors are poised to pounce on the least infraction as an occasion to end an elder’s career and seize a job opening for themselves. Add racial difference as an accelerant, and what was intended as an interesting methodological discussion in a faculty newsletter can explode into a national culture war.

One of the greatest American Africanists was the late Philip Curtin. He wrote one of the first attempts to tally the exact number of persons trafficked by the transatlantic slave trade. Upon publication in 1972, his book was acclaimed as a truly pioneering work of history. By 1995, however, he was moved to protest against trends in the discipline at that time in an article in the Chronicle of Higher Education:I am troubled by increasing evidence of the use of racial criteria in filling faculty posts in the field of African history … This form of intellectual apartheid has been around for several decades, but it appears to have become much more serious in the past few years, to the extent that white scholars trained in African history now have a hard time finding jobs.

Much of academia is governed these days by a joke from the Soviet Union: “If you think it, don’t speak it. If you speak it, don’t write it. If you write it, don’t sign it. But if you do think it, speak it, write it, and sign it—don’t be surprised.”

Yet this silence has consequences, too. One of the most unsettling is the displacement of history by mythmaking

mythmaking is spreading from “just the movies” to more formal and institutional forms of public memory. If old heroes “must fall,” their disappearance opens voids for new heroes to be inserted in their place—and that insertion sometimes requires that new history be fabricated altogether, the “bad history” that Sweet tried to warn against.

If it is not the job of the president of the American Historical Association to confront those questions, then whose is it?

Sweet used a play on words—“Is History History?”—for the title of his complacency-shaking essay. But he was asking not whether history is finished, done with, but Is history still history? Is it continuing to do what history is supposed to do? Or is it being annexed for other purposes, ideological rather than historical ones?

Advocates of studying the more distant past to disturb and challenge our ideas about the present may accuse their academic rivals of “presentism.”

In real life, of course, almost everybody who cares about history believes in a little of each option. But how much of each? What’s the right balance? That’s the kind of thing that historians do argue about, and in the arguing, they have developed some dismissive labels for one another

Those who look to the more recent past to guide the future may accuse the other camp of “antiquarianism.”

The accusation of presentism hurts because it implies that the historian is sacrificing scholarly objectivity for ideological or political purposes. The accusation of antiquarianism stings because it implies that the historian is burrowing into the dust for no useful purpose at all.

In his mind, he was merely reopening one of the most familiar debates in professional history: the debate over why? What is the value of studying the past? To reduce the many available answers to a stark choice: Should we study the more distant past to explore its strangeness—and thereby jolt ourselves out of easy assumptions that the world we know is the only possible one?

Or should we study the more recent past to understand how our world came into being—and thereby learn some lessons for shaping the future?

The August edition of the association’s monthly magazine featured, as usual, a short essay by the association’s president, James H. Sweet, a professor at the University of Wisconsin at Madison. Within hours of its publication, an outrage volcano erupted on social media. A professor at Cornell vented about the author’s “white gaze.”

Colonisation exposed us to new ideas that shocked and confused us. Graeber & Wengrow focus on the French coming into contact with Native Americans in Canada, and in particular on Wendat Confederacy philosopher–statesman Kandiaronk as an example of European traders, missionaries, and intellectuals debating with, and being criticized by indigenous people. Historians have downplayed how much these encounters shaped Enlightenment ideas.

this thought-provoking book is armed to the teeth with fascinating ideas and interpretations that go against mainstream thinking

ather than yet another history book telling you how humanity got here, they take their respective disciplines to task for dealing in myths.

“extensive agriculture may thus have been an outcome, not a cause, of urbanization”

this simplistic tale of progress ignores and downplays that there was nothing linear or inevitable about where we have ended up.

ake agriculture. Rather than humans enthusiastically entering into what Harari in Sapiens called a Faustian bargain with crops, there were many pathways and responses

Experiments show that plant domestication could have been achieved in as little as 20–30 years, so the fact that cereal domestication here took some 3,000 years questions the notion of an agricultural “revolution”. Lastly, this book includes many examples of areas where agriculture was purposefully rejected. Designating such times and places as “pre-agricultural” is misleading, write the authors, they were anti-agricultural.

The idea that agriculture led to large states similarly needs revision

correlation is not causation, and some 15–20 additional centres of domestication have since been identified that followed different paths. Some cities have previously remained hidden in the sediments of ancient river deltas until revealed by modern remote-sensing technology.

Its legacy, shaped via several iterations, is the modern textbook narrative: hunter-gathering was replaced by pastoralism and then farming; the agricultural revolution resulted in larger populations producing material surpluses; these allowed for specialist occupations but also needed bureaucracies to share and administer them to everyone; and this top-down control led to today’s nation states. Ta-daa!

There are so many historical details and delights hiding between these covers that I was thoroughly enthralle

These instead relied on collective decision-making through assemblies or councils, which questions some of the assumptions of evolutionary psychology about scale: that larger human groups require complex (i.e. hierarchical) systems to organize them.

e what is staring them in the face

humans have always been very capable of consciously experimenting with different social arrangements. And—this is rarely acknowledged—they did so on a seasonal basis, spending e.g. part of the year settled in large communal groups under a leader, and another part as small, independently roving bands.

Throughout, Graeber & Wengrow convincingly argue that the only thing we can say about our ancestors is that “there is no single pattern. The only consistent phenomenon is the very fact of alteration […] If human beings, through most of our history, have moved back and forth fluidly between different social arrangements […] maybe the real question should be ‘how did we get stuck?

Next to criticism, the authors put out some interesting ideas of their own, of which I want to quickly highlight two.

The first is that some of the observed variations in social arrangements resulted from schismogenesis. Anthropologist Gregory Bateson coined this term in the 1930s to describe how people define themselves against or in opposition to others, adopting behaviours and attitudes that are different.

The second idea is that states can be described in terms of three elementary forms of domination: control of violence, control of information, and individual charisma, which express themselves as sovereignty, administration, and competitive politics.

Asking which past society most resembles today’s is the wrong question to ask. It risks slipping into an exercise in retrofitting, “which makes us scour the ancient world for embryonic versions of our modern nation states”

I have left unmentioned several other topics: the overlooked role of women, the legacy of Rousseau’s and Hobbes’s ideas, the origins of inequality and the flawed assumptions hiding behind that question

And cities did not automatically imply social stratification. The Dawn of Everything fascinates with its numerous examples of large settlements without ruling classes, such as Ukrainian mega-sites, the Harappan civilization, or Mexican city-states.

If you have any interest in big history, archaeology, or anthropology, this book is indispensable. I am confident that the questions and critiques raised here will remain relevant for a long time to come.

I was particularly impressed by the in-depth critique by worbsintowords on his YouTube channel What is Politics? of (so far) five videos

Over the subsequent four decades, Thatcherites at think tanks like the Institute of Economic Affairs and the Centre for Policy Studies (which Margaret Thatcher helped set up) described the struggle against both the Labour Party and the broader persistence of Socialism in the Communist and non-Communist world as a “war of ideas.”

Thatcherites, known collectively as the ultras, gained fresh blood in the 2010s as a group of Gen Xers too young to experience Thatcherism in its insurgent early years — including the former home secretary Priti Patel, the former foreign secretary Dominic Raab, the former minister of state for universities Chris Skidmore, Mr. Kwarteng and Ms. Truss — attempted to reboot her ideology for the new millennium.

They followed their idol not only in her antagonism to organized labor but also in her less-known fascination with Asian capitalism. In 2012’s “Britannia Unchained,” a book co-written by the group that remains a Rosetta Stone for the policy surprises of the last month, they slammed the Britons for their eroded work ethic and “culture of excuses” and the “cosseted” public sector unions. They praised China, South Korea, Singapore and Hong Kon

“Britannia Unchained” expressed a desire to go back to the future by restoring Victorian values of hard work, self-improvement and bootstrapping.

While the Gen X Thatcherites didn’t scrimp on data, they also saw something ineffable at the root of British malaise. “Beyond the statistics and economic theories,” they wrote, “there remains a sense in which many of Britain’s problems lie in the sphere of cultural values and mind-set.”

As Thatcher herself put it, “Economics are the method; the object is to change the heart and soul.” Britain needed a leap of faith to restore itself.

Ms. Truss and Mr. Kwarteng seemed to have believed that by patching together all of the most radical policies of Thatcherism (while conveniently dropping the need for spending cuts), they would be incanting a kind of magic spell, an “Open sesame” for “global Britain.” This was their Reagan moment, their moment when, as their favorite metaphors put it, a primordial repressed force would be “unchained,” “unleashed” or “unshackled.”But as a leap of faith, it broke the diver’s neck.

the money markets were not waiting for an act of faith in Laffer Curve fundamentalism after all. This was “Reaganism without the dollar.” Without the confidence afforded to the global reserve currency, the pound went into free fall.

ince the 1970s, the world of think tanks had embraced a framing of the world in terms of discrete spaces that could become what they called laboratories for new policies

The mini-budget subjected the entire economy to experimental treatment. This was put in explicit terms in a celebratory post by a Tory journalist and think tanker claiming that Ms. Truss and Mr. Kwarteng had been “incubated” by the Institute of Economic Affairs in their early years and “Britain is now their laboratory.”

The scientists at the bench discovered that the money markets would not only punish left-wing experiments in changing the balance between states and markets, but they were also sensitive to experiments that pushed too far to the right. A cowed Ms. Truss apologized, and Mr. Kwarteng’s successor has reversed almost all of the planned cuts and limited the term for energy supports.

Industry leaders believe the new A.I. systems could be as important as the introduction of the web browser in the early 1990s and could lead to breakthroughs in areas ranging from drug research to education.

But gnawing at many industry insiders is a fear that they are releasing something dangerous into the wild. Generative A.I. can already be a tool for misinformation. Soon, it could be a risk to jobs. Somewhere down the line, tech’s biggest worriers say, it could be a risk to humanity.

“It is hard to see how you can prevent the bad actors from using it for bad things,” Dr. Hinton said.

After the San Francisco start-up OpenAI released a new version of ChatGPT in March, more than 1,000 technology leaders and researchers signed an open letter calling for a six-month moratorium on the development of new systems because A.I technologies pose “profound risks to society and humanity.

Several days later, 19 current and former leaders of the Association for the Advancement of Artificial Intelligence, a 40-year-old academic society, released their own letter warning of the risks of A.I. That group included Eric Horvitz, chief scientific officer at Microsoft, which has deployed OpenAI’s technology across a wide range of products, including its Bing search engine.

Dr. Hinton, often called “the Godfather of A.I.,” did not sign either of those letters and said he did not want to publicly criticize Google or other companies until he had quit his job

Dr. Hinton, a 75-year-old British expatriate, is a lifelong academic whose career was driven by his personal convictions about the development and use of A.I. In 1972, as a graduate student at the University of Edinburgh, Dr. Hinton embraced an idea called a neural network. A neural network is a mathematical system that learns skills by analyzing data. At the time, few researchers believed in the idea. But it became his life’s work.

Dr. Hinton is deeply opposed to the use of artificial intelligence on the battlefield — what he calls “robot soldiers.”

Around the same time, Google, OpenAI and other companies began building neural networks that learned from huge amounts of digital text. Dr. Hinton thought it was a powerful way for machines to understand and generate language, but it was inferior to the way humans handled language.

In 2018, Dr. Hinton and two other longtime collaborators received the Turing Award, often called “the Nobel Prize of computing,” for their work on neural networks.

In 2012, Dr. Hinton and two of his students in Toronto, Ilya Sutskever and Alex Krishevsky, built a neural network that could analyze thousands of photos and teach itself to identify common objects, such as flowers, dogs and cars.

Then, last year, as Google and OpenAI built systems using much larger amounts of data, his view changed. He still believed the systems were inferior to the human brain in some ways but he thought they were eclipsing human intelligence in others.

“Maybe what is going on in these systems,” he said, “is actually a lot better than what is going on in the brain.”

As companies improve their A.I. systems, he believes, they become increasingly dangerous. “Look at how it was five years ago and how it is now,” he said of A.I. technology. “Take the difference and propagate it forwards. That’s scary.”

Until last year, he said, Google acted as a “proper steward” for the technology, careful not to release something that might cause harm. But now that Microsoft has augmented its Bing search engine with a chatbot — challenging Google’s core business — Google is racing to deploy the same kind of technology. The tech giants are locked in a competition that might be impossible to stop, Dr. Hinton said.

His immediate concern is that the internet will be flooded with false photos, videos and text, and the average person will “not be able to know what is true anymore.”

He is also worried that A.I. technologies will in time upend the job market. Today, chatbots like ChatGPT tend to complement human workers, but they could replace paralegals, personal assistants, translators and others who handle rote tasks. “It takes away the drudge work,” he said. “It might take away more than that.”

Down the road, he is worried that future versions of the technology pose a threat to humanity because they often learn unexpected behavior from the vast amounts of data they analyze. This becomes an issue, he said, as individuals and companies allow A.I. systems not only to generate their own computer code but actually run that code on their ow

And he fears a day when truly autonomous weapons — those killer robots — become reality.

“The idea that this stuff could actually get smarter than people — a few people believed that,” he said. “But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.”

Many other experts, including many of his students and colleagues, say this threat is hypothetical. But Dr. Hinton believes that the race between Google and Microsoft and others will escalate into a global race that will not stop without some sort of global regulation.

But that may be impossible, he said. Unlike with nuclear weapons, he said, there is no way of knowing whether companies or countries are working on the technology in secret. The best hope is for the world’s leading scientists to collaborate on ways of controlling the technology. “I don’t think they should scale this up more until they have understood whether they can control it,” he said.

Dr. Hinton said that when people used to ask him how he could work on technology that was potentially dangerous, he would paraphrase Robert Oppenheimer, who led the U.S. effort to build the atomic bomb: “When you see something that is technically sweet, you go ahead and do it.”

He does not say that anymore.

economist J. Bradford DeLong ’82, Ph.D. ’87, looking back on the twentieth century two decades after its end, comes to a similar conclusion but on different grounds.

DeLong, professor of economics at Berkeley, looks to matters of “contingency” and “choice”: at key junctures the economy suffered “bad luck,” and the actions taken by the responsible policymakers were “incompetent.”

these were “the most consequential years of all humanity’s centuries.” The changes they saw, while in the first instance economic, also “shaped and transformed nearly everything sociological, political, and cultural.”

DeLong’s look back over the twentieth century energetically encompasses political and social trends as well; nor is his scope limited to the United States. The result is a work of strikingly expansive breadth and scope

labeling the book an economic history fails to convey its sweeping frame.

The century that is DeLong’s focus is what he calls the “long twentieth century,” running from just after the Civil War to the end of the 2000s when a series of events, including the biggest financial crisis since the 1930s followed by likewise the most severe business downturn, finally rendered the advanced Western economies “unable to resume economic growth at anything near the average pace that had been the rule since 1870.

d behind those missteps in policy stood not just failures of economic thinking but a voting public that reacted perversely, even if understandably, to the frustrations poor economic outcomes had brought them.

Within this 140-year span, DeLong identifies two eras of “El Dorado” economic growth, each facilitated by expanding globalization, and each driven by rapid advances in technology and changes in business organization for applying technology to economic ends

from 1870 to World War I, and again from World War II to 197

fellow economist Robert J. Gordon ’62, who in his monumental treatise on The Rise and Fall of American Economic Growth (reviewed in “How America Grew,” May-June 2016, page 68) hailed 1870-1970 as a “special century” in this regard (interrupted midway by the disaster of the 1930s).

Gordon highlighted the role of a cluster of once-for-all-time technological advances—the steam engine, railroads, electrification, the internal combustion engine, radio and television, powered flight

Pessimistic that future technological advances (most obviously, the computer and electronics revolutions) will generate productivity gains to match those of the special century, Gordon therefore saw little prospect of a return to the rapid growth of those halcyon days.

DeLong instead points to a series of noneconomic (and non-technological) events that slowed growth, followed by a perverse turn in economic policy triggered in part by public frustration: In 1973 the OPEC cartel tripled the price of oil, and then quadrupled it yet again six years later.

For all too many Americans (and citizens of other countries too), the combination of high inflation and sluggish growth meant that “social democracy was no longer delivering the rapid progress toward utopia that it had delivered in the first post-World War II generation.”

Frustration over these and other ills in turn spawned what DeLong calls the “neoliberal turn” in public attitudes and economic policy. The new economic policies introduced under this rubric “did not end the slowdown in productivity growth but reinforced it.

the tax and regulatory changes enacted in this new climate channeled most of what economic gains there were to people already at the top of the income scale

Meanwhile, progressive “inclusion” of women and African Americans in the economy (and in American society more broadly) meant that middle- and lower-income white men saw even smaller gains—and, perversely, reacted by providing still greater support for policies like tax cuts for those with far higher incomes than their own.

Daniel Bell’s argument in his 1976 classic The Cultural Contradictions of Capitalism. Bell famously suggested that the very success of a capitalist economy would eventually undermine a society’s commitment to the values and institutions that made capitalism possible in the first plac

In DeLong’s view, the “greatest cause” of the neoliberal turn was “the extraordinary pace of rising prosperity during the Thirty Glorious Years, which raised the bar that a political-economic order had to surpass in order to generate broad acceptance.” At the same time, “the fading memory of the Great Depression led to the fading of the belief, or rather recognition, by the middle class that they, as well as the working class, needed social insurance.”

what the economy delivered to “hard-working white men” no longer matched what they saw as their just deserts: in their eyes, “the rich got richer, the unworthy and minority poor got handouts.”

As Bell would have put it, the politics of entitlement, bred by years of economic success that so many people had come to take for granted, squeezed out the politics of opportunity and ambition, giving rise to the politics of resentment.

The new era therefore became “a time to question the bourgeois virtues of hard, regular work and thrift in pursuit of material abundance.”

DeLong’s unspoken agenda would surely include rolling back many of the changes made in the U.S. tax code over the past half-century, as well as reinvigorating antitrust policy to blunt the dominance, and therefore outsize profits, of the mega-firms that now tower over key sectors of the economy

He would also surely reverse the recent trend moving away from free trade. Central bankers should certainly behave like Paul Volcker (appointed by President Carter), whose decisive action finally broke the 1970s inflation even at considerable economic cost

Not only Galbraith’s main themes but many of his more specific observations as well seem as pertinent, and important, today as they did then.

What will future readers of Slouching Towards Utopia conclude?

If anything, DeLong’s narratives will become more valuable as those events fade into the past. Alas, his description of fascism as having at its center “a contempt for limits, especially those implied by reason-based arguments; a belief that reality could be altered by the will; and an exaltation of the violent assertion of that will as the ultimate argument” will likely strike a nerve with many Americans not just today but in years to come.

what about DeLong’s core explanation of what went wrong in the latter third of his, and our, “long century”? I predict that it too will still look right, and important.

we’re networked organisms who move around in shifting surroundings, environments that have the power to transform our thinking

Annie Murphy Paul’s new book, “The Extended Mind,” which exhorts us to use our entire bodies, our surroundings and our relationships to “think outside the brain.”

In 2011, she published “Origins,” which focused on all the ways we are shaped by the environment, before birth and minute to minute thereafter.

“In the nature-nurture dynamic, nurture begins at the time of conception. The food the mother eats, the air she breathes, the water she drinks, the stress or trauma she experiences — all may affect her child for better or worse, over the decades to come.”

a down-to-earth take on the science of epigenetics — how environmental signals become catalysts for gene expression

the parallel to this latest book is that the boundaries we commonly assume to be fixed are actually squishy. The moment of a child’s birth, her I.Q. scores or fMRI snapshots of what’s going on inside her brain — all are encroached upon and influenced by outside forces.

awareness of our internal signals, such as exactly when our hearts beat, or how cold and clammy our hands are, can boost our performance at the poker table or in the financial markets, and even improve our pillow talk

“Though we typically think of the brain as telling the body what to do, just as much does the body guide the brain with an array of subtle nudges and prods. One psychologist has called this guide our ‘somatic rudder,’

The “body scan” aspect of mindfulness meditation that has been deployed by the behavioral medicine pioneer Jon Kabat-Zinn may help people lower their heart rates and blood pressure,

techniques that help us pinpoint their signals can foster well-being

Tania Singer has shown how the neural circuitry underlying compassion is strengthened by meditation practice

our thoughts “are powerfully shaped by the way we move our bodies.” Gestures help us understand spatial concepts; indeed, “without gesture as an aid, students may fail to understand spatial ideas at all,”

looking out on grassy expanses near loose clumps of trees and a source of water helps us solve problems. “Passive attention,” she writes, is “effortless: diffuse and unfocused, it floats from object to object, topic to topic. This is the kind of attention evoked by nature, with its murmuring sounds and fluid motions; psychologists working in the tradition of James call this state of mind ‘soft fascination.’”

The chapters on the ways natural and built spaces reflect universal preferences and enhance the thinking process felt like a respite

In sum, the operation would have been fast, free and disrupted nothing. So why the fuss?

“What can you buy if you have unlimited cash? Can you bend every rule? Can you take apart monuments?”

“There’s a principle at stake,”

The first problem was the astounding wealth of Mr. Bezos.

“The Dutch like to say, ‘Acting normal is crazy enough,’

Essentially, Lasch was attacking the notion, fashionable as Americans basked in their seeming victory over the Soviet Union in the Cold War, that history had a direction — and that one would be wise to stay on the “right side” of it.

Francis Fukuyama expressed a version of this triumphalist idea in his famous 1992 book, “The End of History and the Last Man,” in which he celebrated the notion that History with a capital “H,” in the sense of a battle between competing ideas, was ending with communism left to smolder on Ronald Reagan’s famous ash heap.

One of Martin Luther King Jr.’s most frequently quoted lines speaks to a similar thought, albeit in a different context: “T​he arc of the moral universe is long, but it bends toward justice.” Though he had read Lasch, Obama quoted that line often, just as he liked to say that so-and-so would be “on the wrong side of history” if they didn’t live up to his ideals — whether the issue was same-sex marriage, health policy or the Russian occupation of Crimea.

The memo goes on to list two sets of words: “Optimistic Positive Governing Words” and “Contrasting Words,” which carried negative connotations. One of the latter group was the word “liberal,” sandwiched between “intolerant” and “lie.”

So what’s the difference between a progressive and a liberal?To vastly oversimplify matters, liberal usually refers to someone on the center-left on a two-dimensional political spectrum, while progressive refers to someone further left.

But “liberal” has taken a beating in recent decades — from both left and right.

In the late 1980s and 1990s, Republicans successfully demonized the word “liberal,” to the point where many Democrats shied away from it in favor of labels like “conservative Democrat” or, more recently, “progressive.”

“Is the story of the 20th century about the defeat of the Soviet Union, or was it about two world wars and a Holocaust?” asked Matthew Sitman, the co-host of the “Know Your Enemy” podcast, which recently hosted a discussion on Lasch and the fascination many conservatives have with his ideas. “It really depends on how you look at it.”

None of this was an accident. In 1996, Representative Newt Gingrich of Georgia circulated a now-famous memo called “Language: A Key Mechanism of Control.”

The authors urged their readers: “The words and phrases are powerful. Read them. Memorize as many as possible.”

Republicans subsequently had a great deal of success in associating the term “liberal” with other words and phrases voters found uncongenial: wasteful spending, high rates of taxation and libertinism that repelled socially conservative voters.

Many on the left began identifying themselves as “progressive” — which had the added benefit of harking back to movements of the late 19th and early 20th centuries that fought against corruption, opposed corporate monopolies, pushed for good-government reforms and food safety and labor laws and established women’s right to vote.

Allies of Bill Clinton founded the Progressive Policy Institute, a think tank associated with so-called Blue Dog Democrats from the South.

Now, scrambling the terminology, groups like the Progressive Change Campaign Committee agitate on behalf of proudly left-wing candidates

In 2014, Charles Murray, the polarizing conservative scholar, urged readers of The Wall Street Journal’s staunchly right-wing editorial section to “start using ‘liberal’ to designate the good guys on the left, reserving ‘progressive’ for those who are enthusiastic about an unrestrained regulatory state.”

As Sanders and acolytes like Representative Alexandria Ocasio-Cortez of New York have gained prominence over the last few election cycles, many on the left-wing end of the spectrum have begun proudly applying other labels to themselves, such as “democratic socialist.”

To little avail so far, Kazin, the Georgetown historian, has been urging them to call themselves “social democrats” instead — as many mainstream parties do in Europe.“It’s not a good way to win elections in this country, to call yourself a socialist,” he said.

The complaint — filed last month with the Securities and Exchange Commission and the Department of Justice, as well as the FTC — says thousands of employees still had wide-ranging and poorly tracked internal access to core company software, a situation that for years had led to embarrassing hacks, including the commandeering of accounts held by such high-profile users as Elon Musk and former presidents Barack Obama and Donald Trump.

the whistleblower document alleges the company prioritized user growth over reducing spam, though unwanted content made the user experience worse. Executives stood to win individual bonuses of as much as $10 million tied to increases in daily users, the complaint asserts, and nothing explicitly for cutting spam.

Chief executive Parag Agrawal was “lying” when he tweeted in May that the company was “strongly incentivized to detect and remove as much spam as we possibly can,” the complaint alleges.

Zatko described his decision to go public as an extension of his previous work exposing flaws in specific pieces of software and broader systemic failings in cybersecurity. He was hired at Twitter by former CEO Jack Dorsey in late 2020 after a major hack of the company’s systems.

“I felt ethically bound. This is not a light step to take,” said Zatko, who was fired by Agrawal in January. He declined to discuss what happened at Twitter, except to stand by the formal complaint. Under SEC whistleblower rules, he is entitled to legal protection against retaliation, as well as potential monetary rewards.

The complaint from former head of security Peiter Zatko, a widely admired hacker known as “Mudge,” depicts Twitter as a chaotic and rudderless company beset by infighting, unable to properly protect its 238 million daily users including government agencies, heads of state and other influential public figures.

A person familiar with Zatko’s tenure said the company investigated Zatko’s security claims during his time there and concluded they were sensationalistic and without merit. Four people familiar with Twitter’s efforts to fight spam said the company deploys extensive manual and automated tools to both measure the extent of spam across the service and reduce it.

Overall, Zatko wrote in a February analysis for the company attached as an exhibit to the SEC complaint, “Twitter is grossly negligent in several areas of information security. If these problems are not corrected, regulators, media and users of the platform will be shocked when they inevitably learn about Twitter’s severe lack of security basics.”

Zatko’s complaint says strong security should have been much more important to Twitter, which holds vast amounts of sensitive personal data about users. Twitter has the email addresses and phone numbers of many public figures, as well as dissidents who communicate over the service at great personal risk.

This month, an ex-Twitter employee was convicted of using his position at the company to spy on Saudi dissidents and government critics, passing their information to a close aide of Crown Prince Mohammed bin Salman in exchange for cash and gifts.

Zatko’s complaint says he believed the Indian government had forced Twitter to put one of its agents on the payroll, with access to user data at a time of intense protests in the country. The complaint said supporting information for that claim has gone to the National Security Division of the Justice Department and the Senate Select Committee on Intelligence. Another person familiar with the matter agreed that the employee was probably an agent.

“Take a tech platform that collects massive amounts of user data, combine it with what appears to be an incredibly weak security infrastructure and infuse it with foreign state actors with an agenda, and you’ve got a recipe for disaster,” Charles E. Grassley (R-Iowa), the top Republican on the Senate Judiciary Committee,

Many government leaders and other trusted voices use Twitter to spread important messages quickly, so a hijacked account could drive panic or violence. In 2013, a captured Associated Press handle falsely tweeted about explosions at the White House, sending the Dow Jones industrial average briefly plunging more than 140 points.

After a teenager managed to hijack the verified accounts of Obama, then-candidate Joe Biden, Musk and others in 2020, Twitter’s chief executive at the time, Jack Dorsey, asked Zatko to join him, saying that he could help the world by fixing Twitter’s security and improving the public conversation, Zatko asserts in the complaint.

In 1998, Zatko had testified to Congress that the internet was so fragile that he and others could take it down with a half-hour of concentrated effort. He later served as the head of cyber grants at the Defense Advanced Research Projects Agency, the Pentagon innovation unit that had backed the internet’s invention.

But at Twitter Zatko encountered problems more widespread than he realized and leadership that didn’t act on his concerns, according to the complaint.

Twitter’s difficulties with weak security stretches back more than a decade before Zatko’s arrival at the company in November 2020. In a pair of 2009 incidents, hackers gained administrative control of the social network, allowing them to reset passwords and access user data. In the first, beginning around January of that year, hackers sent tweets from the accounts of high-profile users, including Fox News and Obama.

Several months later, a hacker was able to guess an employee’s administrative password after gaining access to similar passwords in their personal email account. That hacker was able to reset at least one user’s password and obtain private information about any Twitter user.

Twitter continued to suffer high-profile hacks and security violations, including in 2017, when a contract worker briefly took over Trump’s account, and in the 2020 hack, in which a Florida teen tricked Twitter employees and won access to verified accounts. Twitter then said it put additional safeguards in place.

This year, the Justice Department accused Twitter of asking users for their phone numbers in the name of increased security, then using the numbers for marketing. Twitter agreed to pay a $150 million fine for allegedly breaking the 2011 order, which barred the company from making misrepresentations about the security of personal data.

After Zatko joined the company, he found it had made little progress since the 2011 settlement, the complaint says. The complaint alleges that he was able to reduce the backlog of safety cases, including harassment and threats, from 1 million to 200,000, add staff and push to measure results.

But Zatko saw major gaps in what the company was doing to satisfy its obligations to the FTC, according to the complaint. In Zatko’s interpretation, according to the complaint, the 2011 order required Twitter to implement a Software Development Life Cycle program, a standard process for making sure new code is free of dangerous bugs. The complaint alleges that other employees had been telling the board and the FTC that they were making progress in rolling out that program to Twitter’s systems. But Zatko alleges that he discovered that it had been sent to only a tenth of the company’s projects, and even then treated as optional.

“If all of that is true, I don’t think there’s any doubt that there are order violations,” Vladeck, who is now a Georgetown Law professor, said in an interview. “It is possible that the kinds of problems that Twitter faced eleven years ago are still running through the company.”

The complaint also alleges that Zatko warned the board early in his tenure that overlapping outages in the company’s data centers could leave it unable to correctly restart its servers. That could have left the service down for months, or even have caused all of its data to be lost. That came close to happening in 2021, when an “impending catastrophic” crisis threatened the platform’s survival before engineers were able to save the day, the complaint says, without providing further details.

One current and one former employee recalled that incident, when failures at two Twitter data centers drove concerns that the service could have collapsed for an extended period. “I wondered if the company would exist in a few days,” one of them said.

The current and former employees also agreed with the complaint’s assertion that past reports to various privacy regulators were “misleading at best.”

The four people familiar with Twitter’s spam and bot efforts said the engineering and integrity teams run software that samples thousands of tweets per day, and 100 accounts are sampled manually.

As the head of security, Zatko says he also was in charge of a division that investigated users’ complaints about accounts, which meant that he oversaw the removal of some bots, according to the complaint. Spam bots — computer programs that tweet automatically — have long vexed Twitter. Unlike its social media counterparts, Twitter allows users to program bots to be used on its service: For example, the Twitter account @big_ben_clock is programmed to tweet “Bong Bong Bong” every hour in time with Big Ben in London. Twitter also allows people to create accounts without using their real identities, making it harder for the company to distinguish between authentic, duplicate and automated accounts.

In the complaint, Zatko alleges he could not get a straight answer when he sought what he viewed as an important data point: the prevalence of spam and bots across all of Twitter, not just among monetizable users.

Zatko cites a “sensitive source” who said Twitter was afraid to determine that number because it “would harm the image and valuation of the company.” He says the company’s tools for detecting spam are far less robust than implied in various statements.

“Agrawal’s Tweets and Twitter’s previous blog posts misleadingly imply that Twitter employs proactive, sophisticated systems to measure and block spam bots,” the complaint says. “The reality: mostly outdated, unmonitored, simple scripts plus overworked, inefficient, understaffed, and reactive human teams.”

For example, they said the company implied that it had destroyed all data on users who asked, but the material had spread so widely inside Twitter’s networks, it was impossible to know for sure

Some employees charged with executing the fight agreed that they had been short of staff. One said top executives showed “apathy” toward the issue.

Zatko’s complaint likewise depicts leadership dysfunction, starting with the CEO. Dorsey was largely absent during the pandemic, which made it hard for Zatko to get rulings on who should be in charge of what in areas of overlap and easier for rival executives to avoid collaborating, three current and former employees said.

For example, Zatko would encounter disinformation as part of his mandate to handle complaints, according to the complaint. To that end, he commissioned an outside report that found one of the disinformation teams had unfilled positions, yawning language deficiencies, and a lack of technical tools or the engineers to craft them. The authors said Twitter had no effective means of dealing with consistent spreaders of falsehoods.

Dorsey made little effort to integrate Zatko at the company, according to the three employees as well as two others familiar with the process who spoke on the condition of anonymity to describe sensitive dynamics. In 12 months, Zatko could manage only six one-on-one calls, all less than 30 minutes, with his direct boss Dorsey, who also served as CEO of payments company Square, now known as Block, according to the complaint. Zatko allegedly did almost all of the talking, and Dorsey said perhaps 50 words in the entire year to him. “A couple dozen text messages” rounded out their electronic communication, the complaint alleges.

Faced with such inertia, Zatko asserts that he was unable to solve some of the most serious issues, according to the complaint.

Some 30 percent of company laptops blocked automatic software updates carrying security fixes, and thousands of laptops had complete copies of Twitter’s source code, making them a rich target for hackers, it alleges.

A successful hacker takeover of one of those machines would have been able to sabotage the product with relative ease, because the engineers pushed out changes without being forced to test them first in a simulated environment, current and former employees said.

“It’s near-incredible that for something of that scale there would not be a development test environment separate from production and there would not be a more controlled source-code management process,” said Tony Sager, former chief operating officer at the cyberdefense wing of the National Security Agency, the Information Assurance divisio

Sager is currently senior vice president at the nonprofit Center for Internet Security, where he leads a consensus effort to establish best security practices.

Zatko stopped the material from being presented at the Dec. 9, 2021 meeting, the complaint said. But over his continued objections, Agrawal let it go to the board’s smaller Risk Committee a week later.

“A best practice is that you should only be authorized to see and access what you need to do your job, and nothing else,” said former U.S. chief information security officer Gregory Touhill. “If half the company has access to and can make configuration changes to the production environment, that exposes the company and its customers to significant risk.”

The complaint says Dorsey never encouraged anyone to mislead the board about the shortcomings, but that others deliberately left out bad news.

When Dorsey left in November 2021, a difficult situation worsened under Agrawal, who had been responsible for security decisions as chief technology officer before Zatko’s hiring, the complaint says.

An unnamed executive had prepared a presentation for the new CEO’s first full board meeting, according to the complaint. Zatko’s complaint calls the presentation deeply misleading.

The presentation showed that 92 percent of employee computers had security software installed — without mentioning that those installations determined that a third of the machines were insecure, according to the complaint.

Another graphic implied a downward trend in the number of people with overly broad access, based on the small subset of people who had access to the highest administrative powers, known internally as “God mode.” That number was in the hundreds. But the number of people with broad access to core systems, which Zatko had called out as a big problem after joining, had actually grown slightly and remained in the thousands.

The presentation included only a subset of serious intrusions or other security incidents, from a total Zatko estimated as one per week, and it said that the uncontrolled internal access to core systems was responsible for just 7 percent of incidents, when Zatko calculated the real proportion as 60 percent.

The complaint says that about half of Twitter’s roughly 7,000 full-time employees had wide access to the company’s internal software and that access was not closely monitored, giving them the ability to tap into sensitive data and alter how the service worked. Three current and former employees agreed that these were issues.

Agrawal didn’t respond to requests for comment. In an email to employees after publication of this article, obtained by The Post, he said that privacy and security continues to be a top priority for the company, and he added that the narrative is “riddled with inconsistences” and “presented without important context.”

On Jan. 4, Zatko reported internally that the Risk Committee meeting might have been fraudulent, which triggered an Audit Committee investigation.

Agarwal fired him two weeks later. But Zatko complied with the company’s request to spell out his concerns in writing, even without access to his work email and documents, according to the complaint.

Since Zatko’s departure, Twitter has plunged further into chaos with Musk’s takeover, which the two parties agreed to in May. The stock price has fallen, many employees have quit, and Agrawal has dismissed executives and frozen big projects.

Zatko said he hoped that by bringing new scrutiny and accountability, he could improve the company from the outside.

“I still believe that this is a tremendous platform, and there is huge value and huge risk, and I hope that looking back at this, the world will be a better place, in part because of this.”

agents will open up many more learning opportunities.

Already, AI can help you pick out a new TV and recommend movies, books, shows, and podcasts. Likewise, a company I’ve invested in, recently launched Pix, which lets you ask questions (“Which Robert Redford movies would I like and where can I watch them?”) and then makes recommendations based on what you’ve liked in the past

Productivity

copilots can do a lot—such as turn a written document into a slide deck, answer questions about a spreadsheet using natural language, and summarize email threads while representing each person’s point of view.

before the sophisticated agents I’m describing become a reality, we need to confront a number of questions about the technology and how we’ll use it.

Helping patients and healthcare workers will be especially beneficial for people in poor countries, where many never get to see a doctor at all.

To create a new app or service, you won’t need to know how to write code or do graphic design. You’ll just tell your agent what you want. It will be able to write the code, design the look and feel of the app, create a logo, and publish the app to an online store

Agents will do even more. Having one will be like having a person dedicated to helping you with various tasks and doing them independently if you want. If you have an idea for a business, an agent will help you write up a business plan, create a presentation for it, and even generate images of what your product might look like

For decades, I’ve been excited about all the ways that software would make teachers’ jobs easier and help students learn. It won’t replace teachers, but it will supplement their work—personalizing the work for students and liberating teachers from paperwork and other tasks so they can spend more time on the most important parts of the job.

Mental health care is another example of a service that agents will make available to virtually everyone. Today, weekly therapy sessions seem like a luxury. But there is a lot of unmet need, and many people who could benefit from therapy don’t have access to it.

I don’t think any single company will dominate the agents business--there will be many different AI engines available.

The real shift will come when agents can help patients do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.

They’ll replace word processors, spreadsheets, and other productivity apps.

Education

For example, few families can pay for a tutor who works one-on-one with a student to supplement their classroom work. If agents can capture what makes a tutor effective, they’ll unlock this supplemental instruction for everyone who wants it. If a tutoring agent knows that a kid likes Minecraft and Taylor Swift, it will use Minecraft to teach them about calculating the volume and area of shapes, and Taylor’s lyrics to teach them about storytelling and rhyme schemes. The experience will be far richer—with graphics and sound, for example—and more personalized than today’s text-based tutors.

your agent will be able to help you in the same way that personal assistants support executives today. If your friend just had surgery, your agent will offer to send flowers and be able to order them for you. If you tell it you’d like to catch up with your old college roommate, it will work with their agent to find a time to get together, and just before you arrive, it will remind you that their oldest child just started college at the local university.

To see the dramatic change that agents will bring, let’s compare them to the AI tools available today. Most of these are bots. They’re limited to one app and generally only step in when you write a particular word or ask for help. Because they don’t remember how you use them from one time to the next, they don’t get better or learn any of your preferences.

The current state of the art is Khanmigo, a text-based bot created by Khan Academy. It can tutor students in math, science, and the humanities—for example, it can explain the quadratic formula and create math problems to practice on. It can also help teachers do things like write lesson plans.

Businesses that are separate today—search advertising, social networking with advertising, shopping, productivity software—will become one business.

other issues won’t be decided by companies and governments. For example, agents could affect how we interact with friends and family. Today, you can show someone that you care about them by remembering details about their life—say, their birthday. But when they know your agent likely reminded you about it and took care of sending flowers, will it be as meaningful for them?

In the computing industry, we talk about platforms—the technologies that apps and services are built on. Android, iOS, and Windows are all platforms. Agents will be the next platform.

A shock wave in the tech industry

Agents won’t simply make recommendations; they’ll help you act on them. If you want to buy a camera, you’ll have your agent read all the reviews for you, summarize them, make a recommendation, and place an order for it once you’ve made a decision.

Agents will affect how we use software as well as how it’s written. They’ll replace search sites because they’ll be better at finding information and summarizing it for you

they’ll be dramatically better. You’ll be able to have nuanced conversations with them. They will be much more personalized, and they won’t be limited to relatively simple tasks like writing a letter.

Companies will be able to make agents available for their employees to consult directly and be part of every meeting so they can answer questions.

AI agents that are well trained in mental health will make therapy much more affordable and easier to get. Wysa and Youper are two of the early chatbots here. But agents will go much deeper. If you choose to share enough information with a mental health agent, it will understand your life history and your relationships. It’ll be available when you need it, and it will never get impatient. It could even, with your permission, monitor your physical responses to therapy through your smart watch—like if your heart starts to race when you’re talking about a problem with your boss—and suggest when you should see a human therapist.

If the number of companies that have started working on AI just this year is any indication, there will be an exceptional amount of competition, which will make agents very inexpensive.

Agents are smarter. They’re proactive—capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.

Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons.

The most exciting impact of AI agents is the way they will democratize services that today are too expensive for most people

The ramifications for the software business and for society will be profound.

In the next five years, this will change completely. You won’t have to use different apps for different tasks. You’ll simply tell your device, in everyday language, what you want to do. And depending on how much information you choose to share with it, the software will be able to respond personally because it will have a rich understanding of your life. In the near future, anyone who’s online will be able to have a personal assistant powered by artificial intelligence that’s far beyond today’s technology.

You’ll also be able to get news and entertainment that’s been tailored to your interests. CurioAI, which creates a custom podcast on any subject you ask about, is a glimpse of what’s coming.

An agent will be able to help you with all your activities if you want it to. With permission to follow your online interactions and real-world locations, it will develop a powerful understanding of the people, places, and activities you engage in. It will get your personal and work relationships, hobbies, preferences, and schedule. You’ll choose how and when it steps in to help with something or ask you to make a decision.

even the best sites have an incomplete understanding of your work, personal life, interests, and relationships and a limited ability to use this information to do things for you. That’s the kind of thing that is only possible today with another human being, like a close friend or personal assistant.

In the distant future, agents may even force humans to face profound questions about purpose. Imagine that agents become so good that everyone can have a high quality of life without working nearly as much. In a future like that, what would people do with their time? Would anyone still want to get an education when an agent has all the answers? Can you have a safe and thriving society when most people have a lot of free time on their hands?

They’ll have an especially big influence in four areas: health care, education, productivity, and entertainment and shopping.

this is like if Russell Moore came out and said that, on the whole, Christianity turns out to be a bad thing. It’s that big of a deal.

Thompson’s case centers around constraints and supply, particularly as they apply to content creation.

In the pre-internet days, creating and distributing content was relatively expensive, which placed content publishers—be they newspapers, or TV stations, or movie studios—high on the value chain.

The internet reduced distribution costs to zero and this shifted value away from publishers and over to aggregators: Suddenly it was more important to aggregate an audience—a la Google and Facebook—than to be a content creator.

Audiences were valuable; content was commoditized.

What has alarmed Thompson is that AI has now reduced the cost of creating content to zero.

what does the world look like when both the creation and distribution of content are zero?

Hellscape

We’re headed to a place where content is artificially created and distributed in such a way as to be tailored to a given user’s preferences. Which will be the equivalent of living in a hall of mirrors.

What does that mean for news? Nothing good.

So the challenges the New York Times face will be different than the challenges that NPR or your local paper face.

It doesn’t really make sense to talk about “news media” because there are fundamental differences between publication models that are driven by scale.

Two big takeaways:

(1) Ad-supported publications will not survive

Zero-cost for content creation combined with zero-cost distribution means an infinite supply of content. The more content you have, the more ad space exists—the lower ad prices go.

Actually, some ad-supported publications will survive. They just won’t be news. What will survive will be content mills that exist to serve ads specifically matched to targeted audiences.

(2) Size is determinative.

The New York Times has a moat by dint of its size. It will see the utility of its soft “news” sections decline in value, because AI is going to be better at creating cooking and style content than breaking hard news. But still, the NYT will be okay because it has pivoted hard into being a subscription-based service over the last decade.

At the other end of the spectrum, independent journalists should be okay. A lone reporter running a focused Substack who only needs four digits’ worth of subscribers to sustain them.

But everything in between? That’s a crapshoot.

Technology writers sometimes talk about the contrast between “builders” and “conservers” — roughly speaking, between those who are most animated by what we stand to gain from technology and those animated by what we stand to lose.

in our moment the builder and conserver types are proving quite mercurial. On issues ranging from Big Tech to medicine, human enhancement to technologies of governance, the politics of technology are in upheaval.

Dispositions are supposed to be basically fixed. So who would have thought that deep blue cities that yesterday were hotbeds of vaccine skepticism would today become pioneers of vaccine passports? Or that outlets that yesterday reported on science and tech developments in reverent tones would today make it their mission to unmask “tech bros”?

One way to understand this churn is that the builder and the conserver types each speak to real, contrasting features within human nature. Another way is that these types each pick out real, contrasting features of technology. Focusing strictly on one set of features or the other eventually becomes unstable, forcing the other back into view.

Something similar might be said about “The Maniac,” Benjamín Labatut’s new novel, whose designated Prometheus is the Hungarian-born polymath John von Neumann, a pioneer of A.I. as well as an originator of game theory.

both narratives are grounded in fact, using the lives and ideas of real people as fodder for allegory and attempting to write a new mythology of the modern world.

Oppenheimer wasn’t a principal author of that theory. Those scientists, among them Niels Bohr, Erwin Schrödinger and Werner Heisenberg, were characters in Labatut’s previous novel, “When We Cease to Understand the World.” That book provides harrowing illumination of a zone where scientific insight becomes indistinguishable from madness or, perhaps, divine inspiration. The basic truths of the new science seem to explode all common sense: A particle is also a wave; one thing can be in many places at once; “scientific method and its object could no longer be prised apart.”

More than most intellectual bastions, the institute is a house of theory. The Promethean mad scientists of the 19th century were creatures of the laboratory, tinkering away at their infernal machines and homemade monsters. Their 20th-century counterparts were more likely to be found at the chalkboard, scratching out our future in charts, equations and lines of code.

The consequences are real enough, of course. The bombs dropped on Hiroshima and Nagasaki killed at least 100,000 people. Their successor weapons, which Oppenheimer opposed, threatened to kill everybody els

on Neumann and Oppenheimer were close contemporaries, born a year apart to prosperous, assimilated Jewish families in Budapest and New York. Von Neumann, conversant in theoretical physics, mathematics and analytic philosophy, worked for Oppenheimer at Los Alamos during the Manhattan Project. He spent most of his career at the Institute for Advanced Study, where Oppenheimer served as director after the war.

the intellectual drama of “Oppenheimer” — as distinct from the dramas of his personal life and his political fate — is about how abstraction becomes reality. The atomic bomb may be, for the soldiers and politicians, a powerful strategic tool in war and diplomacy. For the scientists, it’s something else: a proof of concept, a concrete manifestation of quantum theory.

. Oppenheimer’s designation as Prometheus is precise. He snatched a spark of quantum insight from those divinities and handed it to Harry S. Truman and the U.S. Army Air Forces.

Labatut’s account of von Neumann is, if anything, more unsettling than “Oppenheimer.” We had decades to get used to the specter of nuclear annihilation, and since the end of the Cold War it has been overshadowed by other terrors. A.I., on the other hand, seems newly sprung from science fiction, and especially terrifying because we can’t quite grasp what it will become.

Von Neumann, who died in 1957, did not teach machines to play Go. But when asked “what it would take for a computer, or some other mechanical entity, to begin to think and behave like a human being,” he replied that “it would have to play, like a child.”

MANIAC. The name was an acronym for “Mathematical Analyzer, Numerical Integrator and Computer,” which doesn’t sound like much of a threat. But von Neumann saw no limit to its potential. “If you tell me precisely what it is a machine cannot do,” he declared, “then I can always make a machine which will do just that.” MANIAC didn’t just represent a powerful new kind of machine, but “a new type of life.”

If Oppenheimer took hold of the sacred fire of atomic power, von Neumann’s theft was bolder and perhaps more insidious: He stole a piece of the human essence. He’s not only a modern Prometheus; he’s a second Frankenstein, creator of an all but human, potentially more than human monster.

“Technological power as such is always an ambivalent achievement,” Labatut’s von Neumann writes toward the end of his life, “and science is neutral all through, providing only means of control applicable to any purpose, and indifferent to all. It is not the particularly perverse destructiveness of one specific invention that creates danger. The danger is intrinsic. For progress there is no cure.”

For the well-being of its users, Bejar argued, Meta needed to change course, focusing less on a flawed system of rules-based policing and more on addressing such bad experiences

The company would need to collect data on what upset users and then work to combat the source of it, nudging those who made others uncomfortable to improve their behavior and isolating communities of users who deliberately sought to harm others.

“I am appealing to you because I believe that working this way will require a culture shift,” Bejar wrote to Zuckerberg—the company would have to acknowledge that its existing approach to governing Facebook and Instagram wasn’t working.

During and after Bejar’s time as a consultant, Meta spokesman Andy Stone said, the company has rolled out several product features meant to address some of the Well-Being Team’s findings. Those features include warnings to users before they post comments that Meta’s automated systems flag as potentially offensive, and reminders to be kind when sending direct messages to users like content creators who receive a large volume of messages. 

Meta’s classifiers were reliable enough to remove only a low single-digit percentage of hate speech with any degree of precision.

Bejar was floored—all the more so when he learned that virtually all of his daughter’s friends had been subjected to similar harassment. “DTF?” a user they’d never met would ask, using shorthand for a vulgar proposition. Instagram acted so rarely on reports of such behavior that the girls no longer bothered reporting them. 

Meta’s own statistics suggested that big problems didn’t exist. 

Meta had come to approach governing user behavior as an overwhelmingly automated process. Engineers would compile data sets of unacceptable content—things like terrorism, pornography, bullying or “excessive gore”—and then train machine-learning models to screen future content for similar material.

While users could still flag things that upset them, Meta shifted resources away from reviewing them. To discourage users from filing reports, internal documents from 2019 show, Meta added steps to the reporting process. Meta said the changes were meant to discourage frivolous reports and educate users about platform rules. 

The outperformance of Meta’s automated enforcement relied on what Bejar considered two sleights of hand. The systems didn’t catch anywhere near the majority of banned content—only the majority of what the company ultimately removed

“Please don’t talk about my underage tits,” Bejar’s daughter shot back before reporting his comment to Instagram. A few days later, the platform got back to her: The insult didn’t violate its community guidelines.

Also buttressing Meta’s statistics were rules written narrowly enough to ban only unambiguously vile material. Meta’s rules didn’t clearly prohibit adults from flooding the comments section on a teenager’s posts with kiss emojis or posting pictures of kids in their underwear, inviting their followers to “see more” in a private Facebook Messenger group. 

“Mark personally values freedom of expression first and foremost and would say this is a feature and not a bug,” Rosen responded

Narrow rules and unreliable automated enforcement systems left a lot of room for bad behavior—but they made the company’s child-safety statistics look pretty good according to Meta’s metric of choice: prevalence.

Defined as the percentage of content viewed worldwide that explicitly violates a Meta rule, prevalence was the company’s preferred measuring stick for the problems users experienced.

According to prevalence, child exploitation was so rare on the platform that it couldn’t be reliably estimated, less than 0.05%, the threshold for functional measurement. Content deemed to encourage self-harm, such as eating disorders, was just as minimal, and rule violations for bullying and harassment occurred in just eight of 10,000 views. 

“There’s a grading-your-own-homework problem,”

Meta defines what constitutes harmful content, so it shapes the discussion of how successful it is at dealing with it.”

It could reconsider its AI-generated “beauty filters,” which internal research suggested made both the people who used them and those who viewed the images more self-critical

the team built a new questionnaire called BEEF, short for “Bad Emotional Experience Feedback.

A recurring survey of issues 238,000 users had experienced over the past seven days, the effort identified problems with prevalence from the start: Users were 100 times more likely to tell Instagram they’d witnessed bullying in the last week than Meta’s bullying-prevalence statistics indicated they should.

“People feel like they’re having a bad experience or they don’t,” one presentation on BEEF noted. “Their perception isn’t constrained by policy.

they seemed particularly common among teens on Instagram.

Among users under the age of 16, 26% recalled having a bad experience in the last week due to witnessing hostility against someone based on their race, religion or identity

More than a fifth felt worse about themselves after viewing others’ posts, and 13% had experienced unwanted sexual advances in the past seven days. 

The vast gap between the low prevalence of content deemed problematic in the company’s own statistics and what users told the company they experienced suggested that Meta’s definitions were off, Bejar argued

To minimize content that teenagers told researchers made them feel bad about themselves, Instagram could cap how much beauty- and fashion-influencer content users saw.

Proving to Meta’s leadership that the company’s prevalence metrics were missing the point was going to require data the company didn’t have. So Bejar and a group of staffers from the Well-Being Team started collecting it

And it could build ways for users to report unwanted contacts, the first step to figuring out how to discourage them.

One experiment run in response to BEEF data showed that when users were notified that their comment or post had upset people who saw it, they often deleted it of their own accord. “Even if you don’t mandate behaviors,” said Krieger, “you can at least send signals about what behaviors aren’t welcome.”

But among the ranks of Meta’s senior middle management, Bejar and Krieger said, BEEF hit a wall. Managers who had made their careers on incrementally improving prevalence statistics weren’t receptive to the suggestion that the approach wasn’t working. 

After three decades in Silicon Valley, he understood that members of the company’s C-Suite might not appreciate a damning appraisal of the safety risks young users faced from its product—especially one citing the company’s own data. 

“This was the email that my entire career in tech trained me not to send,” he says. “But a part of me was still hoping they just didn’t know.”

“Policy enforcement is analogous to the police,” he wrote in the email Oct. 5, 2021—arguing that it’s essential to respond to crime, but that it’s not what makes a community safe. Meta had an opportunity to do right by its users and take on a problem that Bejar believed was almost certainly industrywide.

fter Haugen’s airing of internal research, Meta had cracked down on the distribution of anything that would, if leaked, cause further reputational damage. With executives privately asserting that the company’s research division harbored a fifth column of detractors, Meta was formalizing a raft of new rules for employees’ internal communication.

Among the mandates for achieving “Narrative Excellence,” as the company called it, was to keep research data tight and never assert a moral or legal duty to fix a problem.

“I had to write about it as a hypothetical,” Bejar said. Rather than acknowledging that Instagram’s survey data showed that teens regularly faced unwanted sexual advances, the memo merely suggested how Instagram might help teens if they faced such a problem.

The hope that the team’s work would continue didn’t last. The company stopped conducting the specific survey behind BEEF, then laid off most everyone who’d worked on it as part of what Zuckerberg called Meta’s “year of efficiency.

If Meta was to change, Bejar told the Journal, the effort would have to come from the outside. He began consulting with a coalition of state attorneys general who filed suit against the company late last month, alleging that the company had built its products to maximize engagement at the expense of young users’ physical and mental health. Bejar also got in touch with members of Congress about where he believes the company’s user-safety efforts fell short.