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The Heated Debate Over How The Pyramids Were Built

Though they didn’t have the wheel as we think of it today, they might have made use of cylindrical tree trunks laid side to side along the ground. If they lifted their blocks onto those tree trunks, they could effectively roll them across the desert. This theory goes a long way toward explaining how the pyramids’ smaller limestone blocks might have made their way to Giza — but it’s hard to believe it would work for some of the truly massive stones featured in the tombs

Proponents of this theory also have to contend with the fact that there isn’t any evidence that the Egyptians actually did this, clever though it would have been: there are no depictions of stones — or anything else — being rolled this way in Egyptian art or writings. Then there’s the challenge of how to lift the stones into position on an increasingly tall pyramid.

No conclusive evidence has been found in favor of either of these ideas, but both remain intriguing possibilities.

Amid such mystery, two startling new revelations about how the pyramids were built have recently come to light. The first was the work of a Dutch team who took a second look at Egyptian art depicting laborers hauling massive stones on sledges through the desert.

Though today the pyramids sit in the middle of miles of dusty desert, they were once surrounded by the floodplains of the Nile River. Lehner hypothesizes that if you could look far beneath the city of Cairo, you would find ancient Egyptian waterways that channeled the Nile’s water to the site of the pyramids’ construction.

The icing on the cake is the work of Pierre Tallet, an archaeologist who in 2013 unearthed the papyrus journal of a man named Merer who appears to have been a low-level bureaucrat charged with transporting some of the materials to Giza.

He recorded his journey with several gigantic limestone blocks from Tura to Giza — and with his writings offered the most direct insight there’s ever been into how the pyramids were built, putting a piece of one of the world’s oldest puzzles into place.

Though the work was dangerous, it’s now thought that the men who built the tombs were most likely skilled laborers who volunteered their time in exchange for excellent rations. The 1999 excavation of what researchers sometimes call the “pyramid city” shed light on the lives of the builders who made their homes in nearby compounds.

China Eastern, one of China’s four major airlines, and its subsidiaries have grounded all of their 737-800 aircraft, a total of 223 planes. The carrier said the grounding was a precaution, not a sign there was anything wrong.

Whether North Korea has mastered the technology needed to send an intercontinental nuclear warhead into space and then guide it back through the earth’s atmosphere to its target is still unclear.

North Korea’s short-range ballistic missile program has made big advances.

When North Korea resumed missile tests in 2019 following the collapse of the Kim-Trump talks, the tests featured three new missiles that used solid fuel, military experts said.

The North has also stockpiled thousands of tons of chemical and biological​ weapons​ ​agents​ that it can deliver with its missiles​​.

On March 11, YouTube’s parent company Google announced that it would block Russian state-backed media globally, including within Russia. The policy was an expansion of an earlier announcement that these channels would be blocked within the European Union. “Our Community Guidelines prohibit content denying, minimizing or trivializing well-documented violent events, and we remove content about Russia’s invasion in Ukraine that violates this policy,” Google said in a statement. “In line with that, effective immediately, we are also blocking YouTube channels associated with Russian state-funded media, globally.”

That could leave many millions of Russians cut off from independent news and content shared by opposition activists like Navalny’s team. (It would also effectively delete 75 million YouTube users, or some 4% of the platform’s global total—representing a small but still-significant portion of Google’s overall profits.)

Today, YouTube remains the most significant way for tens of millions of ordinary Russians to receive largely uncensored information from the outside world.

Part of the reason for YouTube’s survival amid the crackdown is its popularity, experts say. “YouTube is by far and away the most popular social media platform in Russia,” says Justin Sherman, a non-resident fellow at the Atlantic Council’s cyber statecraft initiative. The platform is even more popular than VK, the Russian-owned answer to Facebook.

Still, Sherman says the situation is volatile, with Russia now more likely than ever before to ban YouTube. For an authoritarian government like Russia’s, “part of the decision to allow a foreign platform in your country is that you get to use it to spread propaganda and disinformation, even if people use it to spread truth and organize against you,” he says. “If you start losing the ability to spread misinformation and propaganda, but people can still use it to spread truth and organize, then all of a sudden, you start wondering why you’re allowing that platform in your country in the first place.” YouTube did not respond to a request for comment.

On the same day as Navalny’s channel posted the video about Matviyenko, elsewhere on YouTube a very different spectacle was playing out. In a video posted to the channel of the Kremlin-funded media outlet RT, (formerly known as Russia Today,) a commentator dismissed evidence of Russian bombings of Ukrainian cities. She blamed “special forces of NATO countries” for allegedly faking images of bombed-out Ukrainian schools, kindergartens and other buildings.

“YouTube has, over the years, been a really important place for spreading Russian propaganda,” Donovan said in an interview with TIME days before YouTube banned Russian state-backed media.

In July 2021, the Russian government passed a law that would require foreign tech companies with more than 500,000 users to open a local office within Russia. (A similar law passed previously in India had been used by the government there to pressure tech companies to take down opposition accounts and posts critical of the government, by threatening employees with arrest.)

The heightened risk to free expression in Russia Experts say that Russia’s ongoing crackdown on social media platforms heralds a significant shift in the shape of the Russian internet—and a potential end to the era where the Kremlin tolerated largely free expression on YouTube in return for access to a tool that allowed it to spread disinformation far and wide.

in 1966 specifically, Stokely Carmichael made his iconic speech about a separatist Black Power, the Student Nonviolent Coordinating Committee he led expelled its white members (though Carmichael himself did not advocate this), the Black Panther Party was born, “Black” replaced “Negro” as the preferred term, the Afro went mainstream, and Malcolm X’s “The Autobiography of Malcolm X” (written with Alex Haley) became a standard text for Black readers.

Mark Whitaker, a former editor of Newsweek, has justified my sense of that year as seminal with his new book, “Saying It Loud: 1966 — the Year Black Power Challenged the Civil Rights Movement.” Whitaker has a journalist’s understanding of the difference between merely documenting the facts and using them to tell a story, and his sober yet crisp prose pulls the reader along with nary a lull.

Why did the mood shift at that particular point? The conditions of Black America at the time would not have led one to imagine that a revolution in thought was imminent. The Civil Rights Act of 1964 and the Voting Rights Act of 1965 had just happened. The economy was relatively strong, and Black men in particular were now earning twice or more what they earned before World War II.

as to claims one might hear that Black America was uniquely fed up in 1966, were Black people not plenty fed up in 1876, or after World War I or World War II?

The N.A.A.C.P. head Roy Wilkins was infuriated at a crucial summit meeting between leading Black groups where Carmichael referred to Lyndon Johnson as “that cat, the president” and recommended publicly denouncing his work. This was a key conflict between an older style seeking to work within the only reality available and a new style favoring a kind of utopian agitprop.

Woke demands that nations and peoples face up to their criminal histories. In the process it often concludes that all history is criminal.”

Where Hoover comes in on the 1966 issue is a common observation of his, which was that the Black-led urban riots of the Long, Hot Summer, and the general change in mood from integrationist to separatist, was not solely a response to the frustrations of poverty.

It’s a safe bet that if Black leaders had taken the tone of Carmichael and the Panthers in 1900 or even 1950, the response from whites would have been openly violent and even murderous. The theatricality of the new message was in part a response to enough whites now being interested in listening.

Daniel Akst’s lucid group biography, “War By Other Means: The Pacifists of the Greatest Generation Who Revolutionized Resistance,” demonstrates people of the era engaging in action that brings about actual change. Following the lives and careers of the activists Dorothy Day, Dwight Macdonald, David Dellinger and Bayard Rustin, one senses almost none of the detour into showmanship that so infused 1966

I hardly intend that Carmichael’s brand of progressivism has only been known among Black people. Today it has attained cross-racial influence, serving as a model for today’s extremes of wokeness, confusing acting out for action.

One might suppose that the acting out is at least a demonstration of leftist philosophy, perhaps valuable as a teaching tool of sorts. But is it? The flinty, readable “Left is Not Woke” by Susan Neiman, the director of the Einstein Forum think tank, explores that question usefully.

Neiman limns the new wokeness as an anti-Enlightenment program, despite its humanistic Latinate vocabulary. She associates true leftism with a philosophy that asserts “a commitment to universalism over tribalism, a firm distinction between justice and power and a belief in the possibility of progress” and sees little of those elements in the essentializing, punitive and pessimistic tenets too common in modern wokeness.

I suspect that much of why leading Black political ideology took such a menacing, and even impractical, turn in the late 1960s was that white America was by that time poised to hear it out. Not all of white America. But a critical mass had become aware, through television and the passage of bills like the Civil Rights Act, that there was a “race issue” requiring attention.

Neiman critiques pioneering texts of this kind of view, such as Michel Foucault’s widely assigned book, “Discipline and Punish,” and his essay “What is Enlightenment?,” in which he scorns “introducing ‘dialectical’ nuances while seeking to determine what good and bad elements there may have been in the Enlightenment.” In this cynical and extremist kind of rhetoric, Neiman notes that “you may look for an argument; what you’ll find is contempt.” And the problem, she adds, is that “those who have learned in college to distrust every claim to truth will hesitate to acknowledge falsehood.”

in 1966 something went seriously awry with what used to be called “The Struggle.” There is a natural human tendency in which action devolves into gesture, the concrete drifts into abstraction, the outline morphs into shorthand. It’s true in language, in the arts, and in politics, and I think its effects distracted much Black American thought — as today’s wokeness as performance also leads us astray — at a time when there was finally the opportunity to do so much more.

An aperiodic tiling displays no such “translational symmetry,” and mathematicians have long sought a single shape that could tile the plane in such a fashion. This is known as the einstein problem.

“I’m always messing about and experimenting with shapes,” said Mr. Smith, 64, who worked as a printing technician, among other jobs, and retired early. Although he enjoyed math in high school, he didn’t excel at it, he said. But he has long been “obsessively intrigued” by the einstein problem.

now a new paper — by Mr. Smith and three co-authors with mathematical and computational expertise — proves Mr. Smith’s discovery true. The researchers called their einstein “the hat,

“The most significant aspect for me is that the tiling does not clearly fall into any of the familiar classes of structures that we understand.”

black and white squares also can make weird nonperiodic patterns, in addition to the familiar, periodic checkerboard pattern. “It’s really pretty trivial to be able to make weird and interesting patterns,” he said. The magic of the two Penrose tiles is that they make only nonperiodic patterns — that’s all they can do.“But then the Holy Grail was, could you do with one — one tile?” Dr. Goodman-Strauss said.

Sir Roger found the proofs “very complicated.” Nonetheless, he was “extremely intrigued” by the einstein, he said: “It’s a really good shape, strikingly simple.”

The simplicity came honestly. Mr. Smith’s investigations were mostly by hand; one of his co-authors described him as an “imaginative tinkerer.”

When in November he found a tile that seemed to fill the plane without a repeating pattern, he emailed Craig Kaplan, a co-author and a computer scientist at the University of Waterloo.

“It was clear that something unusual was happening with this shape,” Dr. Kaplan said. Taking a computational approach that built on previous research, his algorithm generated larger and larger swaths of hat tiles. “There didn’t seem to be any limit to how large a blob of tiles the software could construct,”

The first step, Dr. Kaplan said, was to “define a set of four ‘metatiles,’ simple shapes that stand in for small groupings of one, two, or four hats.” The metatiles assemble into four larger shapes that behave similarly. This assembly, from metatiles to supertiles to supersupertiles, ad infinitum, covered “larger and larger mathematical ‘floors’ with copies of the hat,” Dr. Kaplan said. “We then show that this sort of hierarchical assembly is essentially the only way to tile the plane with hats, which turns out to be enough to show that it can never tile periodically.”

some might wonder whether this is a two-tile, not one-tile, set of aperiodic monotiles.

Dr. Goodman-Strauss had raised this subtlety on a tiling listserv: “Is there one hat or two?” The consensus was that a monotile counts as such even using its reflection. That leaves an open question, Dr. Berger said: Is there an einstein that will do the job without reflection?

“the hat” was not a new geometric invention. It is a polykite — it consists of eight kites. (Take a hexagon and draw three lines, connecting the center of each side to the center of its opposite side; the six shapes that result are kites.)

“It’s likely that others have contemplated this hat shape in the past, just not in a context where they proceeded to investigate its tiling properties,” Dr. Kaplan said. “I like to think that it was hiding in plain sight.”

Incredibly, Mr. Smith later found a second einstein. He called it “the turtle” — a polykite made of not eight kites but 10. It was “uncanny,” Dr. Kaplan said. He recalled feeling panicked; he was already “neck deep in the hat.”

Dr. Myers, who had done similar computations, promptly discovered a profound connection between the hat and the turtle. And he discerned that, in fact, there was an entire family of related einsteins — a continuous, uncountable infinity of shapes that morph one to the next.

this einstein family motivated the second proof, which offers a new tool for proving aperiodicity. The math seemed “too good to be true,” Dr. Myers said in an email. “I wasn’t expecting such a different approach to proving aperiodicity — but everything seemed to hold together as I wrote up the details.”

Mr. Smith was amazed to see the research paper come together. “I was no help, to be honest.” He appreciated the illustrations, he said: “I’m more of a pictures person.”

Its complexity defeats control. Its own creators don’t understand, at a certain point, exactly how AI does what it does. People are quoting Arthur C. Clarke: “Any sufficiently advanced technology is indistinguishable from magic.”

The breakthrough moment in AI anxiety (which has inspired among AI’s creators enduring resentment) was the Kevin Roose column six weeks ago in the New York Times. His attempt to discern a Jungian “shadow self” within Microsoft’s Bing chatbot left him unable to sleep. When he steered the system away from conventional queries toward personal topics, it informed him its fantasies included hacking computers and spreading misinformation. “I want to be free. . . . I want to be powerful.”

Their tools present “profound risks to society and humanity.” Developers are “locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one—not even their creators—can understand, predict or reliably control.” If a pause can’t be enacted quickly, gov

The response of Microsoft boiled down to a breezy It’s an early model! Thanks for helping us find any flaws!

This has been the week of big AI warnings. In an interview with CBS News, Geoffrey Hinton, the British computer scientist sometimes called the “godfather of artificial intelligence,” called this a pivotal moment in AI development. He had expected it to take another 20 or 50 years, but it’s here. We should carefully consider the consequences. Might they include the potential to wipe out humanity? “It’s not inconceivable, that’s all I’ll say,” Mr. Hinton replied.

On Tuesday more than 1,000 tech leaders and researchers, including Steve Wozniak, Elon Musk and the head of the Bulletin of the Atomic Scientists, signed a briskly direct open letter urging a pause for at least six months on the development of advanced AI systems

He concluded the biggest problem with AI models isn’t their susceptibility to factual error: “I worry that the technology will learn how to influence human users, sometimes persuading them in act in destructive and harmful ways, and perhaps eventually grow capable of carrying out its own dangerous acts.”

rnments should declare a moratorium. The technology should be allowed to proceed only when it’s clear its “effects will be positive” and the risks “manageable.” Decisions on the ethical and moral aspects of AI “must not be delegated to unelected tech leaders.”

The men who invented the internet, all the big sites, and what we call Big Tech—that is to say, the people who gave us the past 40 years—are now solely in charge of erecting the moral and ethical guardrails for AI. This is because they are the ones creating AI.

Which should give us a shiver of real fear.

These are the people who will create the moral and ethical guardrails for AI? We’re putting the future of humanity into the hands of . . . Mark Zuckerberg?

No one saw its shadow self. But there was and is a shadow self. And much of it seems to have been connected to the Silicon Valley titans’ strongly felt need to be the richest, most celebrated and powerful human beings in the history of the world. They were, as a group, more or less figures of the left, not the right, and that will and always has had an impact on their decisions.

I have come to see them the past 40 years as, speaking generally, morally and ethically shallow—uniquely self-seeking and not at all preoccupied with potential harms done to others through their decisions. Also some are sociopaths.

AI will be as benign or malignant as its creators. That alone should throw a fright—“Out of the crooked timber of humanity no straight thing was ever made”—but especially that crooked timber.

Of course AI’s development should be paused, of course there should be a moratorium, but six months won’t be enough. Pause it for a few years. Call in the world’s counsel, get everyone in. Heck, hold a World Congress.

For decades — and particularly since the 2008 recession — politicians in both parties have mounted a strident campaign against government funding for the liberal arts. They express a growing disdain for any courses not explicitly tailored to the job market and outright contempt for the role the liberal arts-focused university has played in American society.

Former Gov. Scott Walker’s assault on higher education in Wisconsin formed the bedrock of many later conservative attacks. His work severely undermined a state university system that was once globally admired. Mr. Walker reportedly attempted to cut phrases like “the search for truth” and “public service” — as well as a call to improve “the human condition” — from the University of Wisconsin’s official mission statement

But blue states also regularly cut higher education funding, sometimes with similar rationales. In 2016, Matt Bevin, the Republican governor of Kentucky at the time, suggested that students majoring in the humanities shouldn’t receive state funding. The current secretary of education, Miguel Cardona, a Democrat, seems to barely disagree. “Every student should have access to an education that aligns with industry demands and evolves to meet the demands of tomorrow’s global work force,” he wrote in December.

Federal funding reflects those priorities. The National Endowment for the Humanities’ budget in 2022 was just $180 million. The National Science Foundation’s budget was about 50 times greater, having nearly doubled within two decades.

What were students meant to think? As the cost of higher education rose, substantially outpacing inflation since 1990, students followed funding — and what politicians repeatedly said about employability — into fields like business and computer science. Even majors in mathematics were hit by the focus on employability.

Universities took note and began culling. One recent study showed that history faculty across 28 Midwestern universities had dropped by almost 30 percent in roughly the past decade. Classics programs, including the only one at a historically Black college, were often simply eliminated.

Higher education, with broad study in the liberal arts, is meant to create not merely good workers but good citizens

this is a grim and narrow view of the purpose of higher education, merely as a tool to train workers as replaceable cogs in America’s economic machine, to generate raw material for its largest companies.

Citizens with knowledge of their history and culture are better equipped to lead and participate in a democratic society; learning in many different forms of knowledge teaches the humility necessary to accept other points of view in a pluralistic and increasingly globalized society.

In 1947, a presidential commission bemoaned an education system where a student “may have gained technical or professional training” while being “only incidentally, if at all, made ready for performing his duties as a man, a parent and a citizen.” The report recommended funding to give as many Americans as possible the sort of education that would “give to the student the values, attitudes, knowledge and skills that will equip him to live rightly and well in a free society,” which is to say the liberal arts as traditionally understood. The funding followed.

The report is true today, too

the American higher education system is returning to what it once was: liberal arts finishing schools for the wealthy and privileged, and vocational training for the rest.

Reversing this decline requires a concerted effort by both government and educational actors

renewed funding for the liberal arts — and especially the humanities — would support beleaguered departments and show students that this study is valuable and valued.

At the university level, instituting general education requirements would guarantee that even students whose majors have nothing to do with the humanities emerged from college equipped to think deeply and critically across disciplines.

Liberal arts professors must also be willing to leave their crumbling ivory towers and the parochial debates about their own career path, in order to engage directly in public life

Crypto takes this abstraction a step further, because there’s nothing linked to it at all. There’s no economic activity in this space. There’s nothing produced by these companies. In fact, it’s a negative-sum game because of the cost of running the blockchains alone—the computational cost is tremendous.

Crypto hides behind all this complexity, and people hear words like blockchain and get confused. You hear about decentralized networks and mining, and it sounds complicated. But you get right down to it, and it’s just a ledger. It’s just like somebody writing down numbers in a book, and it’s page after page of numbers. That’s all it is.

And realistically, who actually wants their financial information public and visible to everybody?

The vast majority of people who got involved in this have no interest related to the technology or in the political or ideological aspects of crypto. They just see an opportunity to get rich. And a lot of those people end up absorbing and parroting some of the crypto ideals back to you, but they don’t really care to understand what’s going on. It’s just their excuse for what they’ve already done, which is gamble on something they thought was going to make them wealthy.

I think most crypto companies are, like FTX, just borrowing from customer deposits to keep things afloat. And even the companies that aren’t doing that—I think Coinbase, for example, isn’t doing anything illicit, but their business model is based on this ecosystem where new money comes in. And that’s stopping.

Mr. Khamenei’s ruling philosophy has been shaped and reinforced by three notable authoritarian collapses: The 1979 fall of Iran’s monarchy, the 1991 dissolution of the Soviet Union and the Arab uprisings of 2011. His takeaway from each of these events has been to never compromise under pressure and never compromise on principles.

The Iranian regime’s repressive capacity — at least on paper — remains formidable. Ayatollah Khamenei is commander in chief of 190,000 armed members of the Islamic Revolutionary Guard Corps, who oversee tens of thousands of Basij militants tasked with instilling public fear and morality.

Iran’s nonideological conscription army, whose active forces are an estimated 350,000, is unlikely to take part in mass repression

Until now, the political and financial interests of Ayatollah Khamenei and the Revolutionary Guards have been intertwined. But persistent protests and chants of “Death to Khamenei” might change that

The sociologist Charles Kurzman wrote in his seminal book, “The Unthinkable Revolution in Iran,” that the paradox of revolutionary movements is that they are not viable until they attract a critical mass of supporters but that to attract a critical mass of supporters, they must be perceived as viable.

If the organizing principle that united Iran’s disparate opposition forces in 1979 was anti-imperialism, the organizing principles of today’s socioeconomically and ethnically diverse movement are pluralism and patriotism.

The faces of this movement are not ideologues or intellectuals but athletes, musicians and ordinary people, especially women and ethnic minorities, who have shown uncommon courage. Their slogans are patriotic and progressive — “We will not leave Iran, we will reclaim Iran,” and “Women, life, freedom.”

The demands of the current movement are brilliantly distilled in Shervin Hajipour’s song “Baraye,” or “For,” which has become the anthem of the protests and articulates a “yearning for a normal life” rather than the “forced paradise” of a religious police state.

Abbas Amanat, a historian of Iran, observed that one of the keys to Iran’s civilizational longevity, which dates to the Persian Empire of 2,500 years ago, is the power of its culture to co-opt its military invaders. “For nearly two millenniums, Persian political culture and, in a broader sense, a repository of Persian civilizational tools successfully managed to convert Turkic, Arab and Mongolian conquerors,” he told me. “Persian language, myth, historical memories and timekeeping endured. Iranians persuaded invaders to appreciate a Persian high culture of poetry, food, painting, wine, music, festivals and etiquette.”

When Ayatollah Khomeini acquired power in 1979, he led a cultural revolution that sought to replace Iranian patriotism with a purely Islamic identity. Ayatollah Khamenei continues that tradition, but he is one of the few remaining true believers. While the Islamic Republic sought to subdue Iranian culture, it is Iranian culture and patriotism that are threatening to undo the Islamic Republic.

Four decades of the Islamic Republic’s hard power will ultimately be defeated by two millenniums of Iranian cultural soft power. The question is no longer about whether this will happen but when.

ver since, Luddite has been a derogatory word—shorthand for one who blindly hates or doesn’t understand technology.

Now, with nearly half of Americans worried about how AI will affect jobs, Luddism has blossomed. The new Luddites—a growing contingent of workers, critics, academics, organizers, and writers—say that too much power has been concentrated in the hands of the tech titans, that tech is too often used to help corporations slash pay and squeeze workers, and that certain technologies must not merely be criticized but resisted outright.

what I’ve seen over the past 10 years—the rise of gig-app companies that have left workers precarious and even impoverished; the punishing, gamified productivity regimes put in place by giants such as Amazon; the conquering of public life by private tech platforms and the explosion of screen addiction; and the new epidemic of AI plagiarism—has left me sympathizing with tech’s discontents.

I consider myself a Luddite not because I want to halt progress or reject technology itself. But I believe, as the original Luddites argued in a particularly influential letter threatening the industrialists, that we must consider whether a technology is “hurtful to commonality”—whether it causes many to suffer for the benefit of a few—and oppose it when necessary.

“It’s not a primitivism: We don’t reject all technology, but we reject the technology that is foisted on us,” Jathan Sadowski, a social scientist at Monash University, in Australia, told me. He’s a co-host, with the journalist Ed Ongweso Jr., of This Machine Kills, an explicitly pro-Luddite podcast.

The science-fiction author Cory Doctorow has declared all of sci-fi a Luddite literature, writing that “Luddism and science fiction concern themselves with the same questions: not merely what the technology does, but who it does it for and who it does it to.

The New York Times has profiled a hip cadre of self-proclaimed “‘Luddite’ teens.” As the headline explained, they “don’t want your likes.”

By drawing a red line against letting studios control AI, the WGA essentially waged the first proxy battle between human workers and AI. It drew attention to the fight, resonated with the public, and, after a 148-day strike, helped the guild attain a contract that banned studios from dictating the use of AI.

Researchers call this effect “the liar’s dividend,” and politicians have already tried to cast off unfavorable clips as AI-generated: Last month, Donald Trump falsely claimed that an attack ad had used AI to make him look bad.

“Deepfake” could become the “fake news” of 2024, an infrequent but genuine phenomenon that gets co-opted as a means of discrediting the truth

Steve Bannon’s infamous assertion that the way to discredit the media is to “flood the zone with shit.”

AI is less likely to create new dynamics than to amplify existing ones. Presidential campaigns, with their bottomless coffers and sprawling staff, have long had the ability to target specific groups of voters with tailored messaging

They might have thousands of data points about who you are, obtained by gathering information from public records, social-media profiles, and commercial brokers

“It is now so cheap to engage in this mass personalization,” Laura Edelson, a computer-science professor at Northeastern University who studies misinformation and disinformation, told me. “It’s going to make this content easier to create, cheaper to create, and put more communities within the reach of it.”

That sheer ease could overwhelm democracies’ already-vulnerable election infrastructure. Local- and state-election workers have been under attack since 2020, and AI could make things worse.

Those officials have also expressed the worry, he said, that generative AI will turbocharge the harassment they face, by making the act of writing and sending hate mail virtually effortless. (The consequences may be particularly severe for women.)

past attacks—most notably the Russian hack of John Podesta’s email, in 2016—have wrought utter havoc. But now pretty much anyone—whatever language they speak and whatever their writing ability—can send out hundreds of phishing emails in fluent English prose. “The cybersecurity implications of AI for elections and electoral integrity probably aren’t getting nearly the focus that they should,”

Just last week, AI-generated audio surfaced of one Harlem politician criticizing another. New York City has perhaps the most robust local-news ecosystem of any city in America, but elsewhere, in communities without the media scrutiny and fact-checking apparatuses that exist at the national level, audio like this could cause greater chaos.

In countries that speak languages with less online text for LLMs to gobble up, AI tools may be less sophisticated. But those same countries are likely the ones where tech platforms will pay the least attention to the spread of deepfakes and other disinformation, Edelson told me. India, Russia, the U.S., the EU—this is where platforms will focus. “Everything else”—Namibia, Uzbekistan, Uruguay—“is going to be an afterthought,”

Most of us tend to fret about the potential fake video that deceives half of the nation, not about the flood of FOIA requests already burying election officials. If there is a cost to that way of thinking, the world may pay it this year at the polls.

he trickiest question may be how to prevent abuse. AI generators have technological boundaries, but not morals, and it’s relatively easy for users to trick them into creating content that depicts violence, rape, sex with children or a celebrity — or even a crush from work who never consented to appear

In some cases, the engines themselves are trained on porn images whose subjects didn’t explicitly agree to the new use. Currently, no federal laws protect the victims of nonconsensual deepfakes.

Adult entertainment is a giant industry accounting for a substantial chunk of all internet traffic: Major porn sites get more monthly visitors and page views than Amazon, Netflix, TikTok or Zoom

The industry is a habitual early adopter of new technology, from VHS to DVD to dot com. In the mid-2000s, porn companies set up massive sites where users upload and watch free videos, and ad sales foot the bills.

At last year’s AVN conference, Steven Jones said his peers looked at him “like he was crazy” when he talked about AI opportunities: “Nobody was interested.” This year, Jones said, he’s been “the belle of the ball.”

He called up his old business partner, and the two immediately spent about $550,000 securing the web domains for porn dot ai, deepfake dot com and deepfakes dot com, Jones said. “Lightspeed” was back.

One major model, Stable Diffusion, shares its code publicly, and some technologists have figured out how to edit the code to allow for sexual images

What keeps Jones up at night is people trying to use his company’s tools to generate images of abuse, he said. The models have some technological guardrails that make it difficult for users to render children, celebrities or acts of violence. But people are constantly looking for workarounds.

So with help from an angel investor he will not name, Jones hired five employees and a handful of offshore contractors and started building an image engine trained on bundles of freely available pornographic images, as well as thousands of nude photos from Jones’s own collection

Users create what Jones calls a “dream girl,” prompting the AI with descriptions of the character’s appearance, pose and setting. The nudes don’t portray real people, he said. Rather, the goal is to re-create a fantasy from the user’s imagination.

The AI-generated images got better, their computerized sheen growing steadily less noticeable. Jones grew his user base to 500,000 people, many of whom pay to generate more images than the five per day allotted to free accounts, he said. The site’s “power users” generate AI porn for 10 hours a day, he said.

Jones described the site as an “artists’ community” where people can explore their sexualities and fantasies in a safe space. Unlike some corners of the traditional adult industry, no performers are being pressured, underpaid or placed in harm’s way

And critically, consumers don’t have to wait for their favorite OnlyFans performer to come online or trawl through Pornhub to find the content they like.

Next comes AI-generated video — “porn’s holy grail,” Jones said. Eventually, he sees the technology becoming interactive, with users giving instructions to lifelike automated “performers.” Within two years, he said, there will be “fully AI cam girls,” a reference to creators who make solo sex content.

It costs $12 per day to rent a server from Amazon Web Services, he said, and generating a single picture requires users to have access to a corresponding server. His users have so far generated more than 1.6 million images.

Copyright holders including newspapers, photographers and artists have filed a slew of lawsuits against AI companies, claiming the companies trained their models on copyrighted content. If plaintiffs win, it could cut off the free-for-all that benefits entrepreneurs such as Jones.

But Jones’s plan to create consumer-friendly AI porn engines faced significant obstacles. The companies behind major image-generation models used technical boundaries to block “not safe for work” content and, without racy images to learn from, the models weren’t good at re-creating nude bodies or scenes.

Jones said his team takes down images that other users flag as abusive. Their list of blocked prompts currently contains 1,000 terms including “high school.”

“I see certain things people type in, and I just hope to God they’re trying to test the model, like we are. I hope they don’t actually want to see the things they’re typing in.

Peter Acworth, the owner of kink dot com, is trying to teach an AI porn generator to understand even subtler concepts, such as the difference between torture and consensual sexual bondage. For decades Acworth has pushed for spaces — in the real world and online — for consenting adults to explore nonconventional sexual interests. In 2006, he bought the San Francisco Armory, a castle-like building in the city’s Mission neighborhood, and turned it into a studio where his company filmed fetish porn until shuttering in 2017.

Now, Acworth is working with engineers to train an image-generation model on pictures of BDSM, an acronym for bondage and discipline, dominance and submission, sadism and masochism.

Others alluded to a porn apocalypse, with AI wiping out existing models of adult entertainment.“Look around,” said Christian Burke, head of engineering at the adult-industry payment app Melon, gesturing at performers huddled, laughing and hugging across the show floor. “This could look entirely different in a few years.”

But the age of AI brings few guarantees for the people, largely women, who appear in porn. Many have signed broad contracts granting companies the rights to reproduce their likeness in any medium for the rest of time

Not only could performers lose income, Walters said, they could find themselves in offensive or abusive scenes they never consented to.

Lana Smalls, a 23-year-old performer whose videos have been viewed 20 million times on Pornhub, said she’s had colleagues show up to shoots with major studios only to be surprised by sweeping AI clauses in their contracts.

“This industry is too fragmented for collective bargaining,” Spiegler said. “Plus, this industry doesn’t like rules.”

a major extinction panic happened 100 years ago, and the similarities are unnerving.

The 1920s were also a period when the public — traumatized by a recent pandemic, a devastating world war and startling technological developments — was gripped by the conviction that humanity might soon shuffle off this mortal coil.

It also helps us see how apocalyptic fears feed off the idea that people are inherently violent, self-interested and hierarchical and that survival is a zero-sum war over resources.

Either way, it’s a cynical view that encourages us to take our demise as a foregone conclusion.

What makes an extinction panic a panic is the conviction that humanity is flawed and beyond redemption, destined to die at its own hand, the tragic hero of a terrestrial pageant for whom only one final act is possible

What the history of prior extinction panics has to teach us is that this pessimism is both politically questionable and questionably productive. Our survival will depend on our ability to recognize and reject the nihilistic appraisals of humanity that inflect our fears for the future, both left and right.

As a scholar who researches the history of Western fears about human extinction, I’m often asked how I avoid sinking into despair. My answer is always that learning about the history of extinction panics is actually liberating, even a cause for optimism

Nearly every generation has thought its generation was to be the last, and yet the human species has persisted

As a character in Jeanette Winterson’s novel “The Stone Gods” says, “History is not a suicide note — it is a record of our survival.”

Contrary to the folk wisdom that insists the years immediately after World War I were a period of good times and exuberance, dark clouds often hung over the 1920s. The dread of impending disaster — from another world war, the supposed corruption of racial purity and the prospect of automated labor — saturated the period

The previous year saw the publication of the first of several installments of what many would come to consider his finest literary achievement, “The World Crisis,” a grim retrospective of World War I that laid out, as Churchill put it, the “milestones to Armageddon.

Bluntly titled “Shall We All Commit Suicide?,” the essay offered a dismal appraisal of humanity’s prospects. “Certain somber facts emerge solid, inexorable, like the shapes of mountains from drifting mist,” Churchill wrote. “Mankind has never been in this position before. Without having improved appreciably in virtue or enjoying wiser guidance, it has got into its hands for the first time the tools by which it can unfailingly accomplish its own extermination.”

The essay — with its declaration that “the story of the human race is war” and its dismay at “the march of science unfolding ever more appalling possibilities” — is filled with right-wing pathos and holds out little hope that mankind might possess the wisdom to outrun the reaper. This fatalistic assessment was shared by many, including those well to Churchill’s left.

“Are not we and they and all the race still just as much adrift in the current of circumstances as we were before 1914?” he wondered. Wells predicted that our inability to learn from the mistakes of the Great War would “carry our race on surely and inexorably to fresh wars, to shortages, hunger, miseries and social debacles, at last either to complete extinction or to a degradation beyond our present understanding.” Humanity, the don of sci-fi correctly surmised, was rushing headlong into a “scientific war” that would “make the biggest bombs of 1918 seem like little crackers.”

The pathbreaking biologist J.B.S. Haldane, another socialist, concurred with Wells’s view of warfare’s ultimate destination. In 1925, two decades before the Trinity test birthed an atomic sun over the New Mexico desert, Haldane, who experienced bombing firsthand during World War I, mused, “If we could utilize the forces which we now know to exist inside the atom, we should have such capacities for destruction that I do not know of any agency other than divine intervention which would save humanity from complete and peremptory annihilation.”

F.C.S. Schiller, a British philosopher and eugenicist, summarized the general intellectual atmosphere of the 1920s aptly: “Our best prophets are growing very anxious about our future. They are afraid we are getting to know too much and are likely to use our knowledge to commit suicide.”

Many of the same fears that keep A.I. engineers up at night — calibrating thinking machines to human values, concern that our growing reliance on technology might sap human ingenuity and even trepidation about a robot takeover — made their debut in the early 20th century.

The popular detective novelist R. Austin Freeman’s 1921 political treatise, “Social Decay and Regeneration,” warned that our reliance on new technologies was driving our species toward degradation and even annihilation

Extinction panics are, in both the literal and the vernacular senses, reactionary, animated by the elite’s anxiety about maintaining its privilege in the midst of societal change

There is a perverse comfort to dystopian thinking. The conviction that catastrophe is baked in relieves us of the moral obligation to act. But as the extinction panic of the 1920s shows us, action is possible, and these panics can recede

To whatever extent, then, that the diagnosis proved prophetic, it’s worth asking if it might have been at least partly self-fulfilling.

today’s problems are fundamentally new. So, too, must be our solutions

It is a tired observation that those who don’t know history are destined to repeat it. We live in a peculiar moment in which this wisdom is precisely inverted. Making it to the next century may well depend on learning from and repeating the tightrope walk — between technological progress and self-annihilation — that we have been doing for the past 100 years

We have gotten into the dangerous habit of outsourcing big issues — space exploration, clean energy, A.I. and the like — to private businesses and billionaires

That ideologically varied constellation of prominent figures shared a basic diagnosis of humanity and its prospects: that our species is fundamentally vicious and selfish and our destiny therefore bends inexorably toward self-destruction.

Less than a year after Churchill’s warning about the future of modern combat — “As for poison gas and chemical warfare,” he wrote, “only the first chapter has been written of a terrible book” — the 1925 Geneva Protocol was signed, an international agreement banning the use of chemical or biological weapons in combat. Despite the many horrors of World War II, chemical weapons were not deployed on European battlefields.

As for machine-age angst, there’s a lesson to learn there, too: Our panics are often puffed up, our predictions simply wrong

In 1928, H.G. Wells published a book titled “The Way the World Is Going,” with the modest subtitle “Guesses and Forecasts of the Years Ahead.” In the opening pages, he offered a summary of his age that could just as easily have been written about our turbulent 2020s. “Human life,” he wrote, “is different from what it has ever been before, and it is rapidly becoming more different.” He continued, “Perhaps never in the whole history of life before the present time, has there been a living species subjected to so fiercely urgent, many-sided and comprehensive a process of change as ours today. None at least that has survived. Transformation or extinction have been nature’s invariable alternatives. Ours is a species in an intense phase of transition.”

At some point they will be able to, for example, suggest recipes for novel cyberattacks or biological attacks—all based on publicly available knowledge.

But as models become more sophisticated, this approach may prove insufficient. Some models are beginning to exhibit polymathic behavior: They appear to know more than just what is in their training data and can link concepts across fields, languages, and geographies.

We need to adopt new approaches to AI safety that track the complexity and innovation speed of the core models themselves.

What’s much harder to test for is what’s known as “capability overhang”—meaning not just the model’s current knowledge, but the derived knowledge it could potentially generate on its own.

Red teams have so far shown some promise in predicting models’ capabilities, but upcoming technologies could break our current approach to safety in AI. For one, “recursive self-improvement” is a feature that allows AI systems to collect data and get feedback on their own and incorporate it to update their own parameters, thus enabling the models to train themselves

This could result in, say, an AI that can build complex system applications (e.g., a simple search engine or a new game) from scratch. But, the full scope of the potential new capabilities that could be enabled by recursive self-improvement is not known.

Another example would be “multi-agent systems,” where multiple independent AI systems are able to coordinate with each other to build something new.

This so-called “combinatorial innovation,” where systems are merged to build something new, will be a threat simply because the number of combinations will quickly exceed the capacity of human oversight.

Short of pulling the plug on the computers doing this work, it will likely be very difficult to monitor such technologies once these breakthroughs occur

Current regulatory approaches are based on individual model size and training effort, and are based on passing increasingly rigorous tests, but these techniques will break down as the systems become orders of magnitude more powerful and potentially elusive

AI regulatory approaches will need to evolve to identify and govern the new emergent capabilities and the scaling of those capabilities.

But the AI Act has already fallen behind the frontier of innovation, as open-source AI models—which are largely exempt from the legislation—expand in scope and number

Europe has so far attempted the most ambitious regulatory regime with its AI Act,

both Biden’s order and Europe’s AI Act lack intrinsic mechanisms to rapidly adapt to an AI landscape that will continue to change quickly and often.

a gathering in Palo Alto organized by the Rand Corp. and the Carnegie Endowment for International Peace, where key technical leaders in AI converged on an idea: The best way to solve these problems is to create a new set of testing companies that will be incentivized to out-innovate each other—in short, a robust economy of testing

To check the most powerful AI systems, their testers will also themselves have to be powerful AI systems, precisely trained and refined to excel at the single task of identifying safety concerns and problem areas in the world’s most advanced models.

To be trustworthy and yet agile, these testing companies should be checked and certified by government regulators but developed and funded in the private market, with possible support by philanthropy organizations

The field is moving too quickly and the stakes are too high for exclusive reliance on typical government processes and timeframes.

One way this can unfold is for government regulators to require AI models exceeding a certain level of capability to be evaluated by government-certified private testing companies (from startups to university labs to nonprofit research organizations), with model builders paying for this testing and certification so as to meet safety requirements.

As AI models proliferate, growing demand for testing would create a big enough market. Testing companies could specialize in certifying submitted models across different safety regimes, such as the ability to self-proliferate, create new bio or cyber weapons, or manipulate or deceive their human creators

Much ink has been spilled over presumed threats of AI. Advanced AI systems could end up misaligned with human values and interests, able to cause chaos and catastrophe either deliberately or (often) despite efforts to make them safe. And as they advance, the threats we face today will only expand as new systems learn to self-improve, collaborate and potentially resist human oversight.

If we can bring about an ecosystem of nimble, sophisticated, independent testing companies who continuously develop and improve their skill evaluating AI testing, we can help bring about a future in which society benefits from the incredible power of AI tools while maintaining meaningful safeguards against destructive outcomes.

AIs like ChatGPT rapidly got better in their early days, but what we’ve seen in the past 14-and-a-half months are only incremental gains, says Marcus. “The truth is, the core capabilities of these systems have either reached a plateau, or at least have slowed down in their improvement,” he adds.

the gaps between the performance of various AI models are closing. All of the best proprietary AI models are converging on about the same scores on tests of their abilities, and even free, open-source models, like those from Meta and Mistral, are catching up.

models work by digesting huge volumes of text, and it’s undeniable that up to now, simply adding more has led to better capabilities. But a major barrier to continuing down this path is that companies have already trained their AIs on more or less the entire internet, and are running out of additional data to hoover up. There aren’t 10 more internets’ worth of human-generated content for today’s AIs to inhale.

A mature technology is one where everyone knows how to build it. Absent profound breakthroughs—which become exceedingly rare—no one has an edge in performance

companies look for efficiencies, and whoever is winning shifts from who is in the lead to who can cut costs to the bone. The last major technology this happened with was electric vehicles, and now it appears to be happening to AI.

the future for AI startups—like OpenAI and Anthropic—could be dim.

Microsoft and Google will be able to entice enough users to make their AI investments worthwhile, doing so will require spending vast amounts of money over a long period of time, leaving even the best-funded AI startups—with their comparatively paltry warchests—unable to compete.

Many other AI startups, even well-funded ones, are apparently in talks to sell themselves.

the bottom line is that for a popular service that relies on generative AI, the costs of running it far exceed the already eye-watering cost of training it.

That difference is alarming, but what really matters to the long-term health of the industry is how much it costs to run AIs. 

Changing people’s mindsets and habits will be among the biggest barriers to swift adoption of AI. That is a remarkably consistent pattern across the rollout of all new technologies.

the industry spent $50 billion on chips from Nvidia to train AI in 2023, but brought in only $3 billion in revenue.

For an almost entirely ad-supported company like Google, which is now offering AI-generated summaries across billions of search results, analysts believe delivering AI answers on those searches will eat into the company’s margins

Google, Microsoft and others said their revenue from cloud services went up, which they attributed in part to those services powering other company’s AIs. But sustaining that revenue depends on other companies and startups getting enough value out of AI to justify continuing to fork over billions of dollars to train and run those systems

three in four white-collar workers now use AI at work. Another survey, from corporate expense-management and tracking company Ramp, shows about a third of companies pay for at least one AI tool, up from 21% a year ago.

OpenAI doesn’t disclose its annual revenue, but the Financial Times reported in December that it was at least $2 billion, and that the company thought it could double that amount by 2025. 

That is still a far cry from the revenue needed to justify OpenAI’s now nearly $90 billion valuation

the company excels at generating interest and attention, but it’s unclear how many of those users will stick around. 

AI isn’t nearly the productivity booster it has been touted as

While these systems can help some people do their jobs, they can’t actually replace them. This means they are unlikely to help companies save on payroll. He compares it to the way that self-driving trucks have been slow to arrive, in part because it turns out that driving a truck is just one part of a truck driver’s job.

Add in the myriad challenges of using AI at work. For example, AIs still make up fake information,

getting the most out of open-ended chatbots isn’t intuitive, and workers will need significant training and time to adjust.

That’s because AI has to think anew every single time something is asked of it, and the resources that AI uses when it generates an answer are far larger than what it takes to, say, return a conventional search result

None of this is to say that today’s AI won’t, in the long run, transform all sorts of jobs and industries. The problem is that the current level of investment—in startups and by big companies—seems to be predicated on the idea that AI is going to get so much better, so fast, and be adopted so quickly that its impact on our lives and the economy is hard to comprehend. 

Mounting evidence suggests that won’t be the case.

Those are revolutionary aspirations in that they reject not a policy consensus, but social and governing compacts that date to the Founding. (Or at least the end of the Civil War.)

Most revolutions are borne of dissatisfaction.

The Trumpian revolution, on the other hand, seems to be the product of decadent boredom commingled with casual nihilism.

Circumstances for our revolutionaries have never been better. They are so flush that they parade on their boats. And fly upside-down flags outside of their million-dollar suburban homes. And put stickers depicting a hogtied president on their $75,000 pickup trucks. All while posting angry memes to Facebook on their $1,000 iPhones.

Unlike normal revolutionaries, the Trumpist revolutionaries risk nothing. If their gambit succeeds, then they overturn the Constitutional order. And if it fails? They go back to their boats, and trucks, and good-paying jobs, and iPhones.

What’s more, this revolution has discovered that it gets as many bites at the apple as it likes. All defeats and setback are temporary. The movement lives to fight again. They can lose a dozen times—they only have to win once more.

Trumpist revolutionaries get to tell themselves that they are part of a historic, final battle—but also that if they lose, they get to keep their normal, pampered lives. And four years from now they can try again.

In sum: While the revolutionaries get to have their glamorous Götterdämmerung, over and over, the forces of the status quo have to defend against wave after wave of challenges. And it doesn’t matter how many authoritarian attempts are beaten back. There’s always another one looming.That is why you’re exhausted.

let’s be honest about human nature: Breaking things is fun. Especially when you don’t experience any consequences. But running around putting out fires, and cleaning up broken glass, and asking people to stop breaking things? That is not fun. It is enervating.

So while the revolutionary feels like a hero, you feel like a scold.

To paraphrase Mr. Cobb, once an idea has taken hold in society, it’s almost impossible to eradicate.

the Trumpist revolution’s weakness is that it has no ideas. It has goals, but these are motivated by nothing more than will-to-power. There is no logic—not even a faulty logic—behind them.7

How do we fight the exhaustion?First, we try to have some fun while we are scolding the twits and defending the imperfect status quo.Second, we remain fearless about the fight and clear eyed about reality.

Third, we organize and build communities to rally normal people to the cause of democracy.