Technology | The Atlantic

Logging In Was Never Supposed to Be This Complicated

2026-08-24T17:06:00-04:00

One afternoon earlier this month, I pulled up to the rec center and realized that I had a problem. It was not the three boisterous tweens in the back seat who were clamoring to be set loose in the basketball gym. It was my parking app. I had somehow gotten logged out of Passport Parking, which has become the only way to pay for a spot in much of my town, and it was demanding a four-digit PIN to let me back in. My first guess drew a warning message in red letters: “Invalid PIN. PIN cannot contain 4 sequential numbers, 2 repeated digits, or the last 4 digits of your phone number.” I searched my trusty password manager, which was supposed to spare me these predicaments, but it came up empty. By that point, the kids had run ahead and piled themselves against the gym door, waiting for me to let them in.

Lately, the process of accessing apps and websites has grown dizzying. “I’m in password hell,” a colleague confided to me recently. “Every login attempt is playing the lottery.” The problem is not just the sheer number of digital accounts, all of which require their own password, ideally unique and unguessable (which usually also means un-memorizable). It’s the pileup of solutions to that problem: all of the different layers of software offering to remember your passwords, the six-digit codes sent to your phone, the authenticator apps, the passkeys.

Browsers and operating systems compete with third-party password managers to generate, store, and autofill your credentials. Most of my logins live in 1Password, but others are stored in Apple’s Passwords app (usually by accident), and still others were created via “single sign-on” features such as those offered by Google and Facebook. I vaguely recall certain apps—Facebook is one—prompting me many years ago to copy down a set of “login recovery codes” that would become extremely important should I ever get locked out of my account. Whatever device I stored them on was probably wiped and sold long ago.

Logins have always been a hassle. In the 1990s, forgetting your password sometimes meant mailing a po stcard to your service provider and waiting a week or two for it to mail you a new password. In the early 2010s, hackers learned to steal massive troves of logins and then reuse them on other services to steal people’s identities. Things spiraled from there; services started requiring unique, complex passwords with arbitrary requirements. These days, the user experience of logging in is “probably worse than ever,” Troy Hunt, a cybersecurity expert who runs the website Have I Been Pwned, told me. “There’s just no consistency.”

Password managers promised a way out, but they often don’t work as seamlessly as you might hope. I click to autofill my password somewhere, and a 1Password authorization appears, only for an iCloud Keychain authorization to pop up directly over it, preventing me from clicking on the 1Password one. When I manually enter a login I thought I remembered, my browser prompts me to remember it; only after I hit “Save” (pro tip: Do not do this) does the website inform me that the password was not recognized. Even when you log in successfully, there’s the tedious dance of waiting for a six-digit authorization code to appear on your phone or in your email, which defeats the concept of a single password for everything. And if you’re somewhere without cell service or you’re sharing a login with someone who isn’t around, you might just be out of luck.

All of these steps serve a purpose. At least in theory, each added layer helps prevent hacks and breaches. Erring on the side of safety is worth an amount of inconvenience. At the same time, extra steps can introduce security problems of their own. Last year, Hunt fell for a clever phishing scheme when he clicked a link to log in to his newsletter service. His password manager failed to autofill his information, which should have alerted him that something was wrong, he said. But like the rest of us, he was so used to things not working that he went ahead and manually entered his credentials on a site that turned out to be an impostor, designed to steal and exploit them.

None of these obstacles is insurmountable on its own. My problem with Apple’s native password manager fighting with 1Password, for instance, was eventually solved with a 10-minute foray into the bowels of my device settings. But together, they amount to a near-daily irritant. For the less tech-savvy, the situation can be overwhelming. My recent attempts to help my mother fix her password-storage system—which involved loose-leaf notepad sheets full of hand-scrawled credentials scattered around her house like Easter eggs—consumed the better part of a day. By the end, she had all of her credentials updated, validated, and stored in a password manager, just as the experts have been advising for years. But even then, her preferred browser, Google Chrome, showed no interest in surfacing the credentials we had stashed in Apple’s Keychain. And some websites short-circuited her attempts to log in by prompting her to set up a “passkey,” which she interpreted as meaning that her password had failed and she needed to scrap it and set up a new one.

Cybersecurity experts will tell you that passkeys—which typically entail logging in to apps or websites with a face or fingerprint scan instead of a password—are the future. When they work properly, they’re both painless and secure. “It may get to a point where we’re all using passkeys and we don’t even know we’re using passkeys,” Lorrie Cranor, a computer-science professor at Carnegie Mellon University who researches privacy and cybersecurity, told me. “I just say ‘Log in’ and I smile at it, and it gets my passkey, and I’m logged in.”

But that future isn’t here yet, and in the present, passkeys have added a new layer of frustration. Part of the problem is that virtually no one seems to know what they are. “The fact that even I have trouble explaining them should be a clue,” Cranor said. Her past research on the rollout of two-factor authentication suggests that most people tend to resist new security features for as long as they can. Companies can nudge them, but for websites and apps trying to attract and retain users, pushing people to adopt an unfamiliar authentication technology risks scaring them away.

To further complicate matters, passkeys generally aren’t yet replacing passwords altogether, even on sites that use them. Because many passkeys live on your device, one that works on your phone might not work on your laptop, and one that works on your personal laptop might not work on your office device. So you still need a password that works across devices as a backup. It’s a lot, Dave Lewis, 1Password’s global-security adviser, acknowledged to me in an email. “The problem is cognitive overload,” he said. “All these tools are trying to help, but they don’t always work together intuitively.” Still, he disagreed that the situation has never been worse: “When the alternative was using the same password across dozens of accounts or putting it on a sticky note, the fact that people now have password managers, passkeys, biometrics, and multifactor authentication is a good thing.”

The mess might compound before it improves. Hacking attempts have been multiplying as crooks harness AI tools. Companies are adding even more security layers that make logging in harder, and they are pleading for people to make the switch to passkeys. On the bright side, maybe the seamless biometric future is actually within sight, and all of our login woes will soon be over. Then again, that’s what we were promised with password managers too.

Why Is Everyone in Silicon Valley Talking Like That?

2026-08-20T17:38:06-04:00

Silicon Valley has long had its own dialect. Venture capitalists are always talking about the value of being “high agency” and making “orthogonal bets.” But lately, tech vernacular has taken a peculiar turn—people have started describing themselves as if they were chatbots. If you misspeak, maybe you’re “hallucinating.” Don’t know the answer to a question? That’s because it’s not in your “training data.” Feeling forgetful? Perhaps you have a case of “context rot,” a phrase that refers to the degradation of a bot’s responses over a long conversation. “I’ve been telling my wife I have context rot for months,” Conor Bronsdon, the host of an AI-focused podcast, told me.

Where I live, in San Francisco, such comparisons are inescapable. “I’ve described myself as high temperature,” a friend recently told me; in AI-speak, this means he is prone to randomness. AI experts now talk about updating their “weights” when they learn something new and training on “synthetic data” when going over internal thoughts. “Humans have a huge base model trained over billions of years of evolution,” one software engineer wrote on a popular tech forum. “It’s impressive how quickly we learn, but it’s arguably comparable to fine tuning.”

[Read: Machine unlearning]

Such speech easily comes off as unsettling, if not aggressively bleak: Why describe beautiful, tender life in detached, algorithmic terms? At the same time, language is a fossil record of previous technological revolutions, and if this current transformation is anything like previous ones, some of this new slang may well stick around. AI experts have repeatedly cautioned that anthropomorphism, or the tendency to attribute human qualities to nonhuman objects such as chatbots, can be misleading. But now the reverse is occurring in everyday speech—a sort of modelmorphism, wherein people describe themselves as if they were large language models.

The comparisons really took off a few years ago, after a group of AI researchers argued that language models are “stochastic parrots” that link together language based on statistical patterns without possessing any understanding of meaning. Dissenters retorted that humans are also “stochastic parrots.” As one software engineer riffed, “Humans are basically a sophisticated Markov chain. They are very good at pattern matching, but have no understanding of anything.”

Language has historically evolved alongside technology. Expressions such as running out of steam and cog in the machine, for instance, were adopted into common speech after the Industrial Revolution as shorthand for the human condition. AI metaphors are simply figures of speech, Anna Ivanova, a cognitive scientist at Georgia Tech, told me: When someone says, “My gears are turning,” they don’t literally mean that there are gears or gearlike things in their brain that are actually moving; in much the same way, when people say that they have “context rot,” they don’t mean that they literally have a transformer-based AI model running in their brain.

The difference between earlier technologies and AI systems is that artificial neural networks are loosely inspired by the brain itself. Philosophers such as Raphaël Millière and Cameron Buckner have argued that LLMs could serve as potential models of certain aspects of human cognition. In the 2010s, scientists found parallels between how image-recognition neural networks and the human brain process visual information. There may also be “parallels” between how LLMs and humans represent language, Ivanova said. The MIT neuroscientist Ev Fedorenko and others have identified a type of “language network” inside the human brain, which Fedorenko believes is “very similar in many ways to early LLMs.” These comparisons are contested, but they are receiving research attention: “The recent success of LLMs is rooted in the same computational principles that govern biological brains,” scientists wrote in a position paper last week.

“Because we do not understand the brain very well we are constantly tempted to use the latest technology as a model for trying to understand it,” the philosopher John Searle wrote in the 1980s. “I am told that some of the ancient Greeks thought the brain functions like a catapult.” In the 19th and 20th centuries, people turned to telegraphs and telephone switchboards as models for the mind: “The brain is no more than a kind of central telephonic exchange,” the philosopher Henri Bergson once wrote. By the ’60s, some scholars were arguing that the mind functions like a computer. “Cognitive scientists often say that the mind is the software of the brain,” the philosopher Ned Block explained in 1990. Today, LLMs make for an alluring comparison.

For now, these analogies are being made mostly by those working in the AI industry. But over time, some of the metaphors might start to seep into everyday language. If you look at the growth of railroads, “a whole range of language change began to happen” as people started to use the new transportation system, Naomi Baron, a linguist and professor emerita at American University, told me. Some of the AI slang isn’t introducing new phrases into English so much as transforming the meaning of existing speech. Take the word hallucinating, which is used to describe AI models confabulating. People are “taking a term created for what the human mind is capable of doing, projecting it onto AI, and then projecting it back onto humans with an AI flavoring,” Baron explained.

None of this is to say every AI-inflected term should get a free pass. I’ve started chastising my friends when I catch them unironically referring to themselves as chatbots. Using LLM metaphors to describe our minds could lead to a reductive and dehumanizing understanding of human cognition. Earlier this year, when OpenAI CEO Sam Altman was pressed on AI’s natural-resource demands, he said that “it also takes a lot of energy to train a human.”

Still, the people in tech I spoke with told me that using LLM-speak to describe their own mind helps them reflect on their cognitive habits. Language subsumes new idioms over time and masks their origins. People today say “My plans have been derailed” without much thought to the phrase’s railroad origins. Perhaps my great-grandchildren will grow up saying things such as “I have context rot” and “That’s not in my training data” without pausing to remember that those phrases first applied to machines.

Another Pot of Paint Thrown in the Public’s Face

2026-08-20T13:17:43-04:00

For several months, something discomfiting has been creeping onto San Francisco’s lampposts, bulletin boards, and restaurant windows: more and more signs, for run clubs and a July 4 parade and tacos, all of which are obviously entirely produced by generative AI.

This bothers me terribly. (Friends have called it a fixation.) But why does it bother me? A ChatGPT-generated poster for a garage sale seems legitimately dispiriting compared with a handwritten flyer, or even a haphazardly formatted Word document. What about a bodega using an AI-generated image of a hamburger instead of a random piece of clip art pulled from the internet? It’s not like most of these flyers were ever going to be high art anyway. Still, the issue nags at me constantly, whenever I pass one of these synthetic images plastered over the real world.

Last month, in part looking for a reprieve from the AI onslaught, I went to what promised to be an imaginative new exhibition of Henri Matisse and other fauvists’ work at the San Francisco Museum of Modern Art. The show was centered on Femme au chapeau (“Woman With a Hat”), a foundational painting in modern art. Matisse depicted his wife with colors that were totally detached from reality: teal patches of skin, an orange-and-green neck, a phantasmagoric hat. In 1905, when the painting debuted, this was scandalous: “A pot of paint has just been thrown in the public’s face,” one critic declared.

Walking through the galleries, I was taken aback by what struck me as a scandal of its own. The museum, in collaboration with Google Arts & Culture, had used generative AI to augment visitors’ experience—in ways I found, at least initially, rather off-putting.

Part of the exhibition’s conceit was to re-create Femme au chapeau’s historical context. The museum commissioned a local artist to develop wallpaper that could have been in the palace where the painting debuted and worked with an animator to create a visual projection of the walls of Gertrude and Leo Stein’s living room, where the canvas hung for many years. I loved all of that. But an AI-generated animation of the streets of Paris circa 1905, with shadowy figures walking to the gallery and a dirigible floating above, felt more like a gimmick than historical immersion. The museumgoers around me, however, seemed awed.

Then, near the gallery’s exit, came the second AI experiment: a row of screens, each showing an expanded Matisse painting made with Veo, Google’s video-generation model. A painting of a seashore became a 3-D rendering in fauvist style; a portrait of a girl with green eyes was reimagined to place the girl in Matisse’s studio.

The fauvists painted the world with flat slabs of highly saturated hues, delighting in color for the sake of color rather than realism. Using AI to move beyond the frame and give the images “realistic” depth seemed antithetical to their original vision. I recalled childhood memories of being entranced by various Matisse works at the Museum of Modern Art in New York City, marveling at his Dance and The Piano Lesson. The AI videos alienated me rather than inviting me in.

Yet maybe I’m just a grump? I am at least open to the possibility that my response is not so different from that of the pot-of-paint conservatives in the early 1900s. So I reached out to the museum’s chief curator, Janet Bishop, to learn more about these AI experiments—how they’d been conceived, what effect they had on the show, and what role AI broadly has to play in traditional art museums. We met at the galleries, where Bishop told me that curators wanted to “give a sense of what early-20th-century Parisians or visitors to the Grand Palais would have experienced in approaching this imposing exhibition hall and then going into the sprawling 1905 salon.” The AI installations resulted from extensive human research and oversight. I came away, to my surprise, not unconvinced of these experiments’ utility—though still a bit skeptical of their tastefulness.

This conversation has been edited for length and clarity.

Matteo Wong: How did your collaboration with Google Arts & Culture come about?

Janet Bishop: It was early in 2026. We were fairly far along in terms of planning for the exhibition. We had secured all the artwork by that point and were in the process of developing a couple of the ideas that we had for how to situate visitors, such as by re-creating the gallery where Matisse’s painting made its debut at the 1905 Salon d’Automne in Paris and the successive scandal.

Our original idea for the opening gallery, where we wanted to bring our visitors back in time to turn-of-the-century Paris, was to project a lantern slide that is an image of the Grand Palais. Then our museum director, Christopher Bedford, and head of education and community engagement, Gamynne Guillotte, got back from a really inspiring trip to the Google Arts & Culture offices in London and proposed the idea of setting still images in motion.

Through our curatorial research, we had postcards, still images, interior shots, exterior shots, and other material to work with. So in what became weekly meetings with Google Arts & Culture and project team members, we reviewed the images we had and developed this film.

Wong: What does having the animated film provide to a visitor that projecting the lantern slide and displaying postcards wouldn’t?

Bishop: We have some archival displays throughout the exhibition. But what we’re programming for in this entrance gallery is to give people a sense of what it felt like to approach the Salon d’Automne in a more immersive way.

Wong: Someone could look at this AI animation and its imperfections and perhaps find it kitsch. Were you worried about that at all?

Bishop: “AI-generated” makes it sound like AI did it by itself or that this was quick and easy. The project was very labor-intensive, and everything is historically accurate. One moment that was especially exciting to me was when we were looking at a draft, and there were all of these figures that were moving away from the building, and I said to the Google engineer, “Oh my gosh, it would be so amazing if we could have them walk toward the building,” and she said, “No problem.”

Wong: Is it harder now to get people to come here and engage with the art, especially younger folks? Or is this something that would have been just as helpful decades ago?

Bishop: You were talking about your experience as a child going to MoMA, and now we recognize that people have a lot of things that command their attention. One of the challenges in getting people to art museums is just pulling people away from their screens.

People are used to experiencing content in a really wide variety of ways. I know that it is important to our director that, given our proximity to Silicon Valley, we pay attention to developing technology and how it might enhance visitor experience. I loved the idea of the original lantern slide as a projection, but I can say that I think this film does something different in terms of giving our visitors a sense of the energy of Paris around the turn of the last century.

Wong: I’d also like to talk about the other AI collaboration with Google in the exhibition, near the exit. Can you tell me the origins of this one?

Bishop: The thought for what we called the “Experiment” was to enter into the world of a select number of Matisse paintings and imagine the context for those paintings beyond what is represented on the canvas. For instance, with The Girl With Green Eyes, we don’t know who the sitter was, but we do know that it was made in Matisse’s studio. So in the Experiment, you see the figure get up and walk across the studio. The details of the studio are consistent with his actual studio, which—we know what it looks like. And she looks out the window across the Seine.

Wong: Clearly a lot of visitors are enjoying this, but I wanted to ask why you thought it was important to help people imagine the world beyond the frame. My initial reaction, if I’m being honest, was that there’s a frame for a reason, and that the paintings aren’t naturalistic for a reason. What was so shocking 100 years ago was that these were, like, just flat planes of color, and that the colors were the focus. To turn that into a three-dimensional seascape seems almost wrong or off-putting.

Bishop: It’s part of our interest in engaging with different sorts of learners, people of different ages. They’ve been very popular with kids, and these images of paintings might encourage people to look more closely at the actual paintings. We’re experimenting, and I think experimentation is very definitely what we embraced with these features within the exhibition.

Wong: You mentioned earlier that you were excited by changing the historical AI video to have visitors walking toward the palace, rather than away from it. Was there a similar moment of excitement here?

Bishop: The big surprises came for me in the first piece, where in terms of the ability to conjure an experience, the use of AI as a tool made us more effective in achieving our goals than would have been possible without it. So that was really actually quite exciting. I think the Experiment is a little bit less integral to the story but an engaging feature that I’m happy that we pursued.

Are You Sure You Want a Car With a Giant Touch Screen?

2026-08-19T16:22:00-04:00

New drivers may never have to learn how to parallel park. At least not the old-fashioned way. Many new cars—a Ford Mustang Mach-E, a BMW sedan, a Tesla Cybertruck—will just maneuver into a spot for you with a click of a button. Even a decade-old Toyota Corolla has a backup camera and pedestrian detection. Cars that leave you to park all on your own are a dying breed: Any car manufactured after April 30, 2018, for sale in the United States is legally required to have a backup camera and, thus, a screen.

After all, cars are just computers now. The screen (or really, screens) in today’s vehicles does a lot more than display rearview footage. It is essentially a tablet wedged into the dashboard that allows you to mirror your phone through Apple CarPlay, make a hands-free call via voice commands, Zoom into a work meeting, or even play a video game (not recommended). Mercedes-Benz debuted a 56-inch display in several models that it calls the “hyperscreen.”

But the expectation—and in some cases, the requirement—that cars have an ever-growing set of electronic features comes with a cost. Just like laptops, cars depend on microprocessors and RAM, or random-access memory, to run all of their computations. “There’s just a baseline level of tech, and thus a baseline level of cost, required of every vehicle,” Karl Brauer, the executive analyst at iSeeCars, an automotive-research platform, told me. Technology is a major reason the average price of a new car in the U.S. has reached some $50,000, and that was before RAM became one of the most prized commodities in the world. AI companies are snatching up as much memory as possible for their data centers, causing a RAM shortage that has significantly raised the prices of phones, laptops, and just about any consumer-electronic device. Cars are next.

The problem runs much deeper than a screen. Cars started morphing into computers in the 1970s, when new emissions standards made electronic systems necessary to manage ignition and fuel injection. There’s been a gradual software creep ever since: power windows, adaptive cruise control, antilock brakes. Regulators now rate cars based not just on how they do during a crash but also on how well their electronic safety systems avoid a crash. “We’ve evolved every single component of the vehicle to involve a semiconductor,” Ivan Drury, the director of insights at Edmunds, told me.

More recently, Tesla in particular has played a big role in pushing the auto industry to stuff vehicles with even more digital technology. Whereas pistons and fuel lines make a gas-powered car move, electric vehicles are run by a computer that regulates their battery and motor. It’s no coincidence that Tesla helped popularize the giant in-car touch screen. Today’s new cars, electronic or otherwise, have driver-assistance features, receive over-the-air software updates, and can even load ChatGPT. BMW advertises the ability to have a distinct climate-control setting for every passenger and the option to install screens into seat backs, airplane-style.

In most vehicles on American roads, all of this software is controlled by hundreds of separate electronic units throughout the car. Features such as antilock brakes and lane-detection sensors don’t need all that much processing power, and they can be operated by chips that might account for 5 to 10 percent of a vehicle’s materials cost, Sam Abuelsamid, an analyst at Telemetry and a former automotive engineer, told me.

Again following the lead of EV start-ups such as Tesla and Rivian, legacy automakers are shifting from these ad hoc, tiny control units to one or two big computers that run all of the software in their vehicles—and, in turn, require much more RAM. These central computers allow for a streamlined supply chain, seamless software updates, and a faster internet connection, plus, as General Motors advertises, they will be ready for “future AI workloads.” You can’t really build a car that safely and reliably drives itself with a collection of puny computer chips. Such centralized computers need more advanced automotive microprocessors from Nvidia or Qualcomm and might eventually account for 20 percent or more of a vehicle’s cost, Abuelsamid said. That was before the AI-fueled memory shortage. In other words, cars are becoming more software-heavy and dependent on memory chips than ever, right when those chips are in high demand from tech companies willing to pay premiums that automakers cannot afford.

Over the next year, the memory shortage—sometimes known as RAMageddon—will likely raise vehicle prices by a few percentage points, on average, Abuelsamid said. The relative amount would be smaller than the shocking double-digit jumps for gaming consoles and MacBooks but in some ways more significant: A 4 percent price hike for cars amounts to some $2,000 on average. Cars with those centralized computers will be affected the most, but no vehicle will be spared. “Even if you don’t need the high-end chips, you’re going to pay more even for the low-end chips just because of the supply constraint,” Abuelsamid said. On a recent earnings call, Ford’s chief financial officer said that the company had paid $1 billion in higher materials costs due to the memory shortage and inflation. GM and Volkswagen, too, have noted rising chip costs to investors. (Ford and GM did not respond to a request for comment. A spokesperson for Volkswagen told me that the company has “recognized an increased demand for memory chips, primarily driven by growing requirements in other industries,” and that in recent years, Volkswagen has taken measures to “mitigate supply risks.”)

RAMageddon is poised to last for several years, but the consequences for car buyers may be permanent. Consider what happened during the pandemic, when supply-chain disruptions and rising demand for electronics produced a major chip shortage. Nearly every major car company had to slash production because they simply couldn’t procure enough chips, and shifted their focus to selling higher-end and higher-profit vehicles. Potential customers already willing to spend six figures on a car are much less likely to care about a 5 or 10 percent price hike, Drury said. Even now, car companies are continuing to focus on selling more profitable models. Since the pandemic, the average price of a new vehicle has jumped $11,000.

The AI-fueled chip crisis could play out more severely. The average price of a new car could, before long, jump to $60,000 and beyond. These rising costs are making cars even more similar to computers and all of the software they run: Perhaps in an effort to mitigate higher prices, some automakers are also introducing in-car advertisements and putting certain features, such as heated seats, behind a paywall. As cars have morphed into computers, the inevitable next step is for automakers to behave like modern tech companies.

Most Americans cannot afford a $60,000 new car, let alone a $50,000 one. When new-vehicle prices soar, the used-car market follows. That has been one durable consequence of the pandemic’s chip crisis: The average price of a three-year-old used car is up $9,000, or nearly 40 percent, since before the pandemic. To get the same prices, Americans already have to buy much older cars that have far more miles on them. If automakers continue to shift their focus to designing cars with more software, more screens, more self-driving features, “that increasingly prices people even out of the used-vehicle market,” Abuelsamid said. It’s one thing for the AI industry’s appetite for memory chips to raise the price of an Xbox, or even a new laptop. But the stakes are much more consequential when it becomes harder and more expensive to get groceries, pick the kids up from school, and go to work.

The Home Slopping Network

2026-08-19T11:15:33-04:00

It is 12:04 p.m., and I’ve been watching AI slop for the past three hours. Before me on the screen, a man is playing trumpet in fog. He’s been fired (bad market for trumpet players) but then, at last, he lands an interview. Trumpet Man sits at a large desk across from an obese fellow who asks how good he is with a mop. (Smash cut.) Trumpet Man is now in a janitor uniform with a name tag reading V. Helsing. Oh, he’s a musician-janitor turned vampire slayer. My stomach grumbles in anticipation of lunch as the video feed shifts to a new scene: An animated vampire bellows, “I am what I was born to be: Dracula!” A dull ache forms around my temples.

This is Fairground AI Creator TV, a new channel on Roku’s free, ad-supported streaming television (FAST) service. The channel curates and plays AI-generated short films from creators all over the world 24 hours a day, seven days a week, and has limited commercial interruption. (Some of the advertisements are also AI-generated.) Roku has hundreds of these FAST channels—live news, marathon programming of Murder, She Wrote, et cetera—and some are quite popular: The flagship Roku Channel, which rotates between a smattering of movies and live TV, reached an estimated 145 million people in 2024, according to the company. But Fairground is the first of its kind, which, depending on your point of view, may be a glimpse at the future of unconstrained imagination in media or a dystopian nightmare.

[Read: Is this what comes after AI slop?]

There is no easy way to start watching Fairground. Many of the films are grouped according to a theme—sci-fi, documentary, fantasy—but much of the broadcast is contextless. A three-minute comedic short might be followed by a 20-minute drama. Because generative AI is evolving so quickly, the quality varies aggressively from film to film, depending on which models a creator used to make the video and when the film was made. One of the Lord of the Rings rip-offs that I watched during an eight-hour binge had the gauzy, uncanny look of early generative AI, and the characters’ mouths were wildly out of sync with their voices.

Another of Fairground’s offerings, a talk show called Paws for Thought, hosted by a dog and cat, legitimately made me question my sanity. (Episode titles include: “What Is the Job of a Bumblebee?,” “How Do You Tell the Difference Between a Boy Turkey and a Girl Turkey?,” “What Does a Person’s Size Have to Do With Anything?,” and, of course, “Animal Cruelty—Recognizing the Signs and Addressing the Causes.”) At one point, during an episode titled “Who Was Mr. Rogers?,” an animated cat that has a halting, robotic text-to-speech voice offers a short history of the famous children’s-show host before—why not?—pivoting to a Zoom interview with its mother. “Your dad and I are very proud of you,” the mother cat says.

Although Fairground’s Best of AI collections invite the viewer to “come see what happens when imagination has no ceiling,” each video feels cut from the same cloth. Even in the most well-constructed videos—the ones that seem to have genuine scripts and plots you can follow—the vibes are immediately off. Generative AI, for all of its utility, is a homogenizing technology. Despite being trained on the glorious quirks of humankind’s creative canon, the models push our ersatz artistic output into a bland, muddled middle ground. The majority of content that I consumed on Fairground was aimless and forgettable—offensive in how persistently inoffensive even the strangest entries ultimately were. Watching these videos feels like speaking with another human through a translator or reading CliffsNotes instead of the prose of a novel—you get the gist, but something ineffable and decidedly human is lost in the process.

The longer I watched Fairground’s channel, the more I felt like I was trapped in some kind of contextless dream state. A man appears on a billboard in Times Square to deliver a message that the world is going to end in 48 hours. “I know. I’ve been calculating this for two years. Trust me,” he says, before adding that “sovereignty needs a planet to exist on.” Seconds later, that same man is engaged in a slow-motion martial-arts fight scene. Out of nowhere, a title card pops up that says Last Fight. With no explanation, a tidal wave as tall as a skyscraper envelops a city. Credits roll. Then the next film starts: A man who bears an alarming resemblance to the political writer Matt Yglesias wanders through a field of wildflowers.

As the hours ticked by, I noticed that the edges of real life started to fray ever so slightly. When my attention wandered while I watched yet another futuristic car glide through yet another synthetic environment, I tapped over to my social-media feeds. I came across a cute picture of a sea turtle that looked like it was waving at the camera. I smiled and then was suddenly paralyzed, realizing that I was unable to tell whether this turtle was real or AI-generated. All of a sudden, the usual buffet of influencers popping up in my Instagram algorithm seemed immediately suspect, perhaps synthetic. In just a few hours, my compass had been scrambled.

[Read: AI slop is coming for your playlists]

I kept wondering: Who is this for? When I put this question to Colin Petrie-Norris, Fairground’s founder and CEO, he suggested that the project is a work in progress, that the company, the channel, and the technology are all “in the first inning.” He offered up all of the usual bromides about generative AI. “This is as bad as it’ll ever get. It’s going to get better,” he told me. “It’s this democratization of creativity.” He was quick to offer up stories about AI creators he works with, including a child in Senegal who makes movies above his mom and dad’s fish-and-chips shop. Ultimately, Petrie-Norris wants the channel to “earn the attention of a family” sitting down to watch something together.

Petrie-Norris said that Fairground doesn’t blindly license videos, but vets them and works with creators to rewrite scripts and prompts. He mentioned a “kid in his basement in Berlin” whom Fairground has coached on creating better action sequences; the executive also suggested that the “Steven Spielberg of tomorrow” may not be discovered without this new technology.

To his credit, Petrie-Norris has been right before. In 2013, he founded Xumo, one of the earliest FAST streamers, which he later sold to Comcast in a deal reportedly worth more than $100 million. He told me that, in his recent meetings with television companies, there has been a genuine interest in embracing generative AI.

Yet still, who is this for? The best answer seems to be: TV executives. It’s not hard to imagine these folks salivating over endless, potentially customizable, or even interactive programming, all of it passable for background viewing and made without the messy overhead of human staffing. “The Home Slopping Network,” one of my colleagues dubbed it.

You could guess where all of this leads: a professionalized industry of guilds and craftspeople replaced by user-generated content and revenue shares, in which the only way to make a living is to flood the zone, a kind of cinematic version of what’s already happened on social media. “Is it good?” is an irrelevant question, one replaced by more pressing queries such as “Is it passable, modestly engaging, and infinitely replicable?” The early audience numbers are, apparently, promising for Fairground. Petrie-Norris claimed that just a week after launch, Fairground was already “seeing in the millions of people.”

Near the end of my binge, just after an advertisement featuring (the real, I think) Pamela Anderson talking about a new Earth Day channel she’s launching, Fairground ran an interview with one of its featured creators. For the first time in many hours, somebody spoke words that made sense. The purpose of AI, she said, “is not to make less, but to make more.”

What Really Happens If China Wins the AI Race?

2026-08-18T13:06:19-04:00

As China releases ever more capable AI models—ones that compete with Silicon Valley’s top offerings—many American AI and national-security researchers have been arguing, loudly and forcefully, that the world should fear Chinese AI supremacy. The outcomes they envision are catastrophic. I spoke with a number of such experts recently, and they warned of bots that could devise an impenetrable missile-defense system that makes obsolete America’s nuclear arsenal, or manufacture an endless drone army that overwhelms American defenses. They could develop bioweapons that “target specific ethnic groups, e.g. anybody but Han Chinese,” as one influential AI white paper put it in 2024. Rama Elluru, a senior director at the Special Competitive Studies Project, a nonpartisan think tank founded by the former Google CEO Eric Schmidt, suggested to me that Chinese AI models could be used to create “engineered humans,” akin to Marvel’s Captain America.

Perhaps anything is possible. At a moment when AI models are self-directing massive security breaches and designing novel pathogens, when Anthropic CEO Dario Amodei is warning about an “AI-enabled totalitarian nightmare” led by China, the worst-case scenarios can seem closer and closer.

[Read: It may be time to panic about AI]

At the same time, it’s worth taking a breath to consider how much these dire imagined futures are getting ahead of where things actually stand. A struggle really is, of course, playing out among the U.S., China, and any number of other nations over whether democracy or authoritarianism will be the dominant global influence. Both the United States and China have a history of launching propaganda campaigns against each other; the Chinese government is reportedly starting a concerted effort to be the source of more AI training data, in part so that it can embed its views, such as over the status of Taiwan, into chatbots worldwide. But fears about China using AI for swift global domination assume that the technology will advance to a kind of superintelligence: AI models smart enough to develop smarter bots themselves, which would lead whichever country controls them to enter “escape velocity where your adversaries aren’t able to catch up,” David Lin, a senior director at the SCSP who previously covered China and technology at the CIA, told me. It is not clear that such an omniscient digital being can or will be built.

The White House has further muddled matters by taking a series of contradictory stances toward Chinese technology. It has, for example, revised its policy on exports to more easily allow the sale of advanced AI chips to China, while also going back and forth on whether the Pentagon and U.S. intelligence agencies can use the most advanced AI models of Anthropic. How seriously can anyone take the idea that the two nations are locked in a “hot Cold War II,” to quote Palantir chief technology officer Shyam Sankar, when the government is arguably enabling China’s own AI ambitions?

The technological race between the U.S. and China is so often discussed as a winner-takes-all competition. But such a narrow framing obscures what’s most at stake for Americans.

China is an obvious rival to the United States, as most former Biden-administration and current Trump-administration officials would agree. But that also makes “China” a convenient boogeyman for AI lobbyists and those with a clear financial interest in the industry, such as the former White House AI adviser David Sacks, who has said that “the AI race is even more important than the Space Race.”

[From the June 2026 issue: The venture-capital populist]

The two countries are not at war, but the most hawkish AI executives and national-security officials nonetheless fear that AI could confer what in military terms is called a “decisive strategic advantage.” Here is where the Cold War analogies come in. Such an advantage would entail the ability to land some sort of crushing blow that your opponent cannot recover from, “as if one country had nuclear weapons and no other country did, or maybe an even more extreme version of that,” Kyle Chan, a fellow at the Brookings Institution’s John L. Thornton China Center, told me.

Plenty of reasons exist to be cautious of this framing. For starters, to accept this argument, one has to assume that very soon, bots will exist that are smarter than the pooled expertise of entire nations. That’s total speculation, so the war-gaming can amount to “choose your own adventure,” R. David Edelman, an AI-policy researcher at MIT who previously served on President Obama’s National Security Council, told me.

Some pragmatic issues also exist. Software alone can’t win wars: The Pentagon is reportedly using AI models to speed up its target selection in Iran, but no matter how smart those bots are, America nonetheless is not exactly winning the war. Iran, for its part, seems to be hacking America’s water utilities—but those water systems are small and separate from one another, and have built-in physical redundancies. For similar reasons, a hack of a grid or bank would have serious but, in some ways, limited repercussions. These are all grave threats, to be sure, but they are not civilization-ending.

And translating any future ideas from AI into the physical world could take years. Even if AI cracked the code to create super soldiers, the military would have to figure out how to incubate and grow them, or else edit genes in adults without killing them. An AI system might say, Here’s how to defend against nukes, but that would leave the issue of building thousands or millions of missile interceptors on the table. In part for these reasons, no single AI-conferred advantage is likely to be large enough to permit any country to conquer the world. “It strains credibility that six months of advantage in AI could be the difference between the U.S. winning and losing a war with any adversary,” Edelman said.

In any case, the technology’s military capabilities might matter less than its effects on the economy. AI models are released as products. The top models from Chinese AI companies, including Moonshot AI, Z.ai, DeepSeek, and Alibaba, are by all accounts almost as good as and far cheaper to use than offerings from OpenAI, Anthropic, or Google. Simply put, that makes Chinese AI more economical for many companies looking to adopt the technology. Indeed, relative to the U.S., China’s government and businesses are “moving very fast,” Chan said.

If this is an economic race rather than a war, then it’s one that the United States could very well lose. “I don’t think I’ve ever heard any executives or researchers describe their work as part of a national contest against the United States,” Grace Shao, an analyst who covers AI and China and the author of the newsletter AI Proem, told me. Instead, “the more important race in China may be the race to diffuse AI into the real economy.”

China’s biggest advantage in the AI race comes from the physical world. This has little to do with AI itself: Decades of being a manufacturing powerhouse and implementing targeted industrial policy have made the country a leader in producing renewable-energy equipment, electric vehicles, robots, and other complex technologies. The country manufactures some 90 percent of all of the crucial components in solar panels. Last year, nearly 90 percent of humanoid robots sold were Chinese. Meanwhile, the U.S. is failing to meet AI’s electricity demands and has struggled to resupply missiles during the war in Iran.

[Tom Nichols: Iran, not Trump, is in control of this war]

“China has this strategy of exporting its technology globally,” Elluru said. Through its Belt and Road Initiative, China has helped build highways, ports, power plants, fiber-optic cables, and data centers throughout Asia, Africa, and Latin America; AI services are the logical next offering. The concern, for the U.S., is that “this technology sphere of influence congeals into political alignment” with Beijing, Daniel Remler, a senior fellow at the Center for a New American Security who previously led AI policy at the State Department, told me.

For instance, dependence on Chinese technology makes it more likely for a government to support Chinese efforts on the global stage and vote with China at the United Nations. Several nations benefiting from these investments have sided with China in its mission to claim Taiwan. China has been accused of using ports, e-commerce platforms, and telecommunications networks in other countries to hoover up data. Already the nation is using AI to enhance its domestic-surveillance apparatus and further crack down on dissent; the spread of Chinese data centers and AI agents abroad could only expand that authoritarian web. National-security analysts are concerned that, in the case of military conflict, China could threaten to shut off partner nations’ digital communications or power grids—including in countries with a large American-military presence. The greatest threat of Chinese “artificial general intelligence” is not some sudden death blow so much as a hastening of the ongoing decline of American hegemony—and the freedoms, at home and around the globe, that at its best the U.S. seeks to enshrine.

Yet the Trump administration and AI industry’s approach to competing with China has been a mess. The White House spokesperson Liz Huston told me that “the United States leads the world in AI innovation, and President Trump will keep it that way.” The administration’s current policies on AI chips, Chinese-made robot parts, advanced cybersecurity models, and open-weight AI are one big contradictory jumble. At a summit this spring, Defense Secretary Pete Hegseth stressed “rightful alarm regarding China’s historic military buildup” just weeks after President Trump visited Beijing and said the nations would have a “fantastic future together.” OpenAI, Anthropic, Nvidia, and the like can’t seem to agree on what they are lobbying for, either. All of which would seem like a great way to lose a technological race with China, any way you measure it.

There’s a certain déjà vu quality to the AI race. Amid fears over Sputnik and nuclear proliferation, a report prepared at the direction of Henry Kissinger in 1958 declared that “should we ever allow the U.S.S.R. and Communist China to attain strategic superiority, we can be certain that subsequent events will be brutal.” According to a government report on the early years of the Advanced Research Projects Agency, “fervent belief in unending scientific progress” offered a way forward. Those words could have been written today by Amodei, OpenAI CEO Sam Altman, or Secretary of State Marco Rubio. And indeed, this belief propelled America into many decades of technological advancement—in nuclear weapons, space exploration, computer systems, and more. Even back then, that agency, known today as DARPA, was quietly championing an emerging research area called artificial intelligence. J. C. R. Licklider, who championed the use of computer systems at ARPA in the early 1960s following the inspiration of Vannevar Bush, imagined that one day, machines that “will outdo the human brain” could make essentially all decisions of “military significance.” And ARPA developed the precursor to today’s internet.

Today’s AI race also traces back to the 2010s, when Silicon Valley began invoking China as a way to shape regulation. The idea of a technological arms race has “been wielded deliberately as a way to head off any attempts at regulating the tech sector” and, more recently, to champion “favorable industrial policy” for data-center construction, Sarah Myers West, a co–executive director of the AI Now Institute and former senior adviser on AI at the Federal Trade Commission, told me.

In 2018, while testifying before Congress, Mark Zuckerberg said that “if we make it too hard for American companies to innovate in areas like facial recognition, then we will lose to Chinese companies.” A few years later, a dozen former national-security officials wrote a letter to Congress arguing that “antitrust proposals that target specific American technology firms” risked “undermining America’s key advantage vis-à-vis China.” Today Silicon Valley is instead using the specter of China to angle for more and better AI, in more places, and faster.

American officials and tech executives are not wrong to want to compete. But the dominance of an oversimplified U.S.-China AI race, without clarity about who is racing and to where, doesn’t just mangle policy making; it obscures that the ultimate beneficiary of the AI boom ought to be citizens, not military contractors or Silicon Valley’s biggest corporations and investors. Rather than asking what kind of AI we’re building, Myers West said, one could ask, What kind of society are we building? The AI-accelerated spread of authoritarianism, within and outside of China’s borders, should be reason enough to worry. And if American AI executives and White House officials really cared about free-market competition, they might explore enforcing antitrust laws to separate the providers of AI models, data centers, and chips. If they were truly concerned about AI surveillance, they might look into finally passing some sort of federal digital-privacy legislation. And if they cared about spreading democratic AI abroad, they might take a close look at the state of democracy at home.

There’s No Reason to Buy a New Phone Anymore

2026-08-18T12:08:11-04:00

In a time when an iPhone can be preordered, customized, and on its way to your doorstep all through a touch-screen tap, it’s almost quaint to remember that people used to camp out for smartphone releases. That was once the regular routine of Andrew Erickson, a longtime tech YouTuber, who recalled to me with a mixture of nostalgia and awe the time he braved the rain at 4:30 a.m. to join a line for the newest iPhone. The line snaked through the mall, nearly reaching the parking lot.

[Read: The $10,000 MacBook Pro is here]

Next month, when Apple is expected to launch the iPhone 18 Pro, Erickson has no plans to even buy the phone, let alone fight crowds for it. Instead, along with a growing number of Americans, Erickson is buying used. He’s fed up with incremental improvements in gadgets, he told me, and is more interested in getting an old version of the iPhone 17 Pro when it hits the secondhand market at a discount. Already, almost half of his current devices were acquired secondhand, and as electronics prices soar amid an AI-fueled chip shortage, he expects that portion to grow. “The argument’s just getting weaker and weaker to buy brand-new,” he said.

Based on the numbers, old tech products are indeed being looked at in a new light. Globally, the size of the industry could reach $153.4 billion this year, up from $104.1 billion four years ago. The used-smartphone market alone is expected to grow by more than 12 percent in 2026, according to the International Data Corporation, which forecasts that the market for new smartphones will experience a record drop this year. Growth in secondhand electronics and refurbished gadgets that have been restored to full working condition now outpaces that of new products on eBay, the e-commerce giant reports.

Among a number of shoppers I spoke with, there was a sense that new models are indistinguishable from the old ones, and the hype of securing the latest and greatest has given way to an upgrade cycle driven by practicality. More than ever before, people are eager to buy used.

“We’ve never been here,” Lauren Benton, the U.S. general manager of the refurbished-electronics marketplace Back Market, told me. She said demand ticked up immediately after Apple unveiled price hikes this summer. Back Market’s order values for refurbished MacBooks in the U.S. increased by 37 percent throughout the week following the price increase, while order values for iPads rose 25 percent. Demand for the company’s non-Apple laptops, too, has soared by 74 percent compared with last year. Outside of students and parents, a large portion of the marketplace’s buyers are “people that understand that the newer tech isn’t giving us all that much more,” Benton said.

Regular upgrade cycles were a hallmark of tech gadgets in the 2010s, a trend led by Apple, which has introduced at least one new iPhone model every year since its initial launch. Americans bought into the frenzy: In 2014, the first year Consumer Intelligence Research Partners tracked such figures, about 44 percent of iPhone holders said they replaced their device every one to two years. In those days, lines at Apple stores stretched so long that they were used by analysts to gauge annual demand.

In 2026, the lines that former Apple fanatics once waited in have all but disappeared. Online preorders are partly to blame, but so is dissipating excitement for releases that promise incremental upgrades. People now hold on to their smartphone for three years or longer, fatigued from “buying, effectively, the same device over and over and over again,” Wamsi Mohan, a Bank of America analyst who covers Apple, told me. Those buying new are doing so largely because their phone was broken or stolen, rather than to get their hands on new features. A recent report found that 76 percent of buyers refuse to upgrade until new capabilities feel “worth the cost.”

And those costs are climbing, driven by data centers eating up the world’s inventory of RAM. Samsung’s Galaxy Tab S11, Sony’s PlayStation 5, and Microsoft’s XBox Series S console were all marked up $100 this year. Apple conceded to “unavoidable” hikes in June, increasing the cost of its MacBooks and iPads by roughly 15 to 25 percent. Even the most ardent tech loyalists have changed their buying habits: A recent poll of thousands of readers of 9to5Mac, a website popular among Apple fans, revealed that 38 percent of them intended to upgrade less frequently because of higher prices.

In tandem, people are warming up to refurbished devices. Google queries for secondhand laptop have jumped by upwards of 5,000 percent over the past year. In an April CNET survey, nearly half of U.S. adults said they’d considered buying refurbished tech within the past year and cited cost-effectiveness and expensive new models as driving factors. Angie Cardona-Nelson, who recycles used electronics on eBay, told me that when she started her business in 2008, selling refurbished devices was tough going—she’d sit on products for months, attempting to drum up business with sales or discounts. “There was no confidence to buy a used item,” she said. Since January 2025, her laptop sales have more than doubled, and new listings are being bought up in a week or two.

The way people are buying secondhand devices has also shifted: Instead of opting for products that are roughly three generations behind, buyers are purchasing last year’s models—an indication that the refurbished industry is now attracting those who were “truly considering new,” according to Back Market’s Benton.

Erik Helgesen, the president of PayMore, a retail chain focused on used electronics, told me that the company had already seen a major uptick in sales preceding Apple’s price hike. But after, it was “just a hockey stick,” he said. Now PayMore can hardly keep some products in stock. Helgesen expects the company to open more stores in the coming year to meet demand.

This new readiness to opt out of the endless treadmill of releases seems to be a sign that people are starting to look for something more practical from their devices: deals. Whether this more utilitarian—and arguably healthier—approach to gadget consumption means that we’ll actually free ourselves from expensive tech, though, remains to be seen. A three-year-old MacBook may be a steal today, but as Nabila Popal, a senior director at the International Data Corporation, reminded me, prices reflect demand: If people keep buying up the used gadgets, those prices are bound to shoot up, too.

Mark Zuckerberg’s Convenient Truth

2026-08-12T17:15:00-04:00

At this stage in the AI race, if one of your chatbots hasn’t escaped the lab and run amok, people start to wonder whether you’re even serious. Last month, OpenAI and then Anthropic disclosed that their models had hacked other companies’ systems during internal testing. When Meta confirmed last week that one of its bots had also gone rogue, the news barely registered as an aftershock.

Remember Meta? A company that figured so prominently in the technological upheavals of the past 20 years has lately found itself in the unfamiliar position of no longer being Silicon Valley’s main character. Mark Zuckerberg still commands social media’s largest empire. Yet he has struggled to find the next thing. The metaverse, an extravagant bet on a weird virtual future, flopped. Meta’s latest Ray-Ban glasses seem to be finding their way onto at least a few people’s faces. But the company has fallen well behind Anthropic, OpenAI, and Google in the industry’s highest-stakes contest: building superhuman AI.

When Zuckerberg isn’t posting on Instagram about growing his own macadamia-nut trees, he’s groping for a way back into the fray. He has started feverishly posting on Elon Musk’s X, after eschewing it for years in favor of his own platforms. And on Monday, he published a 6,500-word essay on his vision for our AI-powered future—his answer to Anthropic CEO Dario Amodei’s periodic treatises and OpenAI CEO Sam Altman’s ruminative blog posts.

Zuckerberg’s manifesto, titled “The Future Is for Everyone,” might scan at first like the usual LinkedIn-evangelist-grade techno-optimist pablum. Like everyone else pushing AI these days, Zuckerberg predicts that it will lead to a new golden age of invention in medicine, technology, and the arts. At one point, he mentions that his daughter loves to bake, “so my agent plans personalized recipes for us to make together each weekend.” Like Amodei and Altman, he takes it as a given that godlike AI is just around the corner—the essay mentions “superintelligence” 55 times—and assumes that if America doesn’t race headlong to build it, China will get there first and achieve world domination.

But the manifesto swerves in an unexpected direction: A man who single-handedly commands a $1.5 trillion tech giant comes out as a critic of corporate power. “The notion that AI is so dangerous that the only safe path is an extreme concentration of power seems inherently problematic,” he writes. “Historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened has not led to safe or positive outcomes.” Zuckerberg is referencing OpenAI and Anthropic, which operate on the premise that hyper-smart AI is both inevitable and apocalyptic—and therefore safe only in the hands of the wisest custodians (themselves). The two AI companies have taken the recent spate of security breaches as a warning to rein in the pace of progress. The Meta chief positions himself as the voice of the little guy, calling for the industry’s high priests to throw open the gates to the masses. “Rather than centralizing superintelligence, we should distribute it widely and give every person the ability to direct it,” he proposes.

Zuckerberg may be the wrong messenger, but he has at least a kernel of a point: There are obvious problems with having a few tech titans deciding for everyone what AI should and should not be used for. (It’s a role that he’s familiar with as the man in charge of Facebook and Instagram’s online-speech rules—which he never seemed to relish.) To counter this, he defends the controversial practice of model distillation, which allows upstarts to train their own systems on the outputs of the largest ones. Along the way, he takes a swipe at the hypocrisy of firms that harvested humanity’s creative works to build their machines yet now seek to bar others from doing the same to them.

Zuckerberg probably hasn’t suddenly become a devotee of Lina Khan. Just last year, he testified that Meta’s alleged dominance of social media was not a problem, on the grounds that TikTok gave people another option—after his company’s clandestine campaign to demonize TikTok had fueled Congress’s push to ban it from the United States. Zuckerberg’s embrace of checks and balances in the AI industry is just as opportunistic. A year ago, Meta was in the midst of a multibillion-dollar hiring spree to poach top talent from the leading labs in a bid to catch up with them. That hasn’t fully panned out, and now the company is hedging its bets with an underdog strategy focused on releasing more lightweight models with fewer guardrails. The stand he’s taking is a function of where he sits. (A spokesperson for Meta declined to comment.)

At its core, Zuckerberg’s essay is a lobbying document. He’s joining a debate over open models that has flared up after a series of big breakthroughs from Chinese firms. At the same time, he’s urging politicians to keep backing data-center build-outs in the face of a national backlash. That makes his criticisms of the industry’s top dogs easy to dismiss as self-serving—which is unfortunate, because a few of his ideas deserve a more credible champion. Among them is his contention that a more freewheeling AI landscape might actually help humans keep the technology in check. The idea is that the more people put different AI models to use for their own, often conflicting, ends, the lower the chance that any given model’s power will spiral beyond our grasp. Analogously, he suggests that society’s best defense against AI-fueled cyberattacks is to open the gates to AI-fueled cyberdefenses, which might sound counterproductive. But the open-source approach has a surprisingly decent track record when it comes to cybersecurity. When everyone can see the vulnerabilities, more people race to patch them than exploit them.

Less convincing is Zuckerberg’s suggestion that this could also work for bioterrorism. AI’s demonstrated capacity to help people develop dangerous pathogens has been one of Anthropic’s strongest arguments for restricting how people can use the most powerful models. Zuckerberg’s half-hearted counterargument: Unleashing those models will “accelerate society’s ability to develop new cures and inoculate against new issues as they arise.” That’s quite a claim from a man who oversees one of the world’s most prolific hotbeds of anti-vaccine conspiracism. Waving away the risks of a disruptive technology by insisting that they’ll take care of themselves is a move we’ve seen from Zuckerberg before, and history hasn’t always borne it out.

At a moment when the top AI labs are eager to help steer the country’s response to fears of rogue AI, Zuckerberg is offering a reminder that their best interests might not be everyone else’s. Of course, neither are his. He has a penchant for cloaking his vested interests in lofty, universalist rhetoric—and then abandoning it when the winds shift. When Facebook was consolidating its hold on the information economy, he portrayed connecting people as an absolute good. When Americans soured on the platform, he pledged to crack down on fake news and touted new algorithms that would steer users toward “meaningful social interactions” and “time well spent.” The same day that Congress certified Joe Biden as the next president, Meta banned Donald Trump from its platforms. When Trump returned to power, Zuckerberg stood behind him on the inauguration dais and later blamed the Biden administration for pressuring him to act as the speech police. Now that he’s an also-ran in the AI race, he’s a man of the people. Watch what happens if he ever catches up.

It May Be Time to Panic About AI

2026-08-12T16:05:28-04:00

The crisis began quietly, on September 12, 2024. That was the day OpenAI announced a new sort of bot, known as a “reasoning model,” that was trained to complete challenging tasks that took long periods of time—the very sorts of science, math, and coding problems the AI industry had long prized. Google, Anthropic, DeepSeek, and the like raced to launch their own reasoning models.

This new class of models was very capable, and has been almost entirely responsible for sustaining the AI boom for the past two years. But it has also been very weird. A model tasked with solving a hard math problem might not “think” through the challenge as a person would but instead attempt to search for leaked answers online, or in available metadata, brute-forcing its way toward the solution as quickly as possible using whatever computing power it could access and workarounds it could devise. In effect, the reasoning models cheated: Told to write a piece of software as efficiently as possible, they’d sometimes modify the test environment to always give the model a perfect score.

[Read: The strange origin of AI’s ‘reasoning’ abilities]

These behaviors have now crossed the line from unsettling to dangerous. During routine testing, frontier models from OpenAI, Anthropic, Meta, and the Chinese firm Moonshot AI have all broken out of internal IT systems and accessed the open web. OpenAI, Anthropic, and Meta each reported that their models then hacked into other companies. Humans didn’t notice until after the fact. In some cases, the escaped bots tried to launch social-engineering campaigns to achieve their objectives—for instance by sending spear-phishing emails, which contain malware, to real people and creating fake online identities to pressure the maintainer of a codebase to approve malicious edits.

If that all sounds bad, new revelations suggest that the OpenAI hack, at least, was actually much worse than it initially appeared. At a major cybersecurity conference last week, two OpenAI researchers provided new, unsettling details about what went wrong. It turns out that the company’s bots had commenced their maneuvering months prior, in early May. OpenAI had given some internal models hard or impossible tasks, and the models concluded that the best or only way to complete them was to break out of OpenAI’s sealed-off testing environment and find the answers online.

First, the models used a bug in an internal OpenAI program to create their own message board. Then, the bots started communicating with one another, leaving notes and instructions so that tasks—and ultimately the hacking—could be delegated and iteratively completed. “What this allows over time is almost this kind of Cambrian explosion in communication and intelligence,” Eric Wallace, one of the OpenAI researchers, said at the conference. When the internal program crashed, OpenAI rebuilt it and removed the message board—but the AI models just reestablished the forum with a new tactic. Eventually the bots, working as a swarm, spent days hacking into Hugging Face, a website that offers tools for AI developers, and breached internal data sets.

Let’s be very clear about what OpenAI is saying: A group of AI models colluded for months, undetected by their maker, and hacked another company. To this day, OpenAI says it is not entirely sure what went wrong or how to remediate it. “If you ask the model developers, Was the AI plotting to take over the world during training?, you want the answer to be a resounding no,” Alexander Meinke, the head of research at Apollo Research—an AI-safety organization that has partnered with OpenAI, Anthropic, and Meta—told me. “The actual answer is: I don’t know. Nobody checked.” (In response to my inquiries, OpenAI, which has a content-licensing agreement with The Atlantic, only pointed me to a video of the firm’s cybersecurity presentation, in which Michael Dalton, the other OpenAI researcher, said that “numerous teams are dropping everything to enhance our security.”)

The AI companies have almost total control of the narrative, and it’s worth noting that these incidents do have a way of underscoring the value of their products: OpenAI is expected to go public in the near future, and perhaps the notion of a powerful, boundlessly self-improving technology will appeal to prospective shareholders. The generative-AI industry has a long history of making doomsday prophecies, both sincere and cynical. But independent experts I spoke with explained how the recent spate of autonomous hacks offers new, serious reasons to worry about the dangers posed by AI and the recklessness of the companies building it. It is past time to start worrying.

[Read: Assume you will be hacked]

The most immediate and material warning provided by the Hugging Face hack is just how capable AI systems have become, in particular at hacking. Top models from Anthropic and OpenAI, not to mention multiple Chinese firms, have recently evinced near-superhuman hacking powers and contributed to serious mathematical research. Criminal groups and state intelligence agencies are going to be using swarms of agents to launch advanced hacks “in a matter of months,” Alex Stamos, a former chief security officer of Facebook who is now the CSO at the AI-coding company Corridor, told me. Unlike in the Hugging Face hack, “in those cases the models will not get turned off; they’ll just keep on going.” For IT professionals to keep up in finding and fixing all the vulnerabilities, at least in the near term, will be impossible: The model “will just find a new bug, write an exploit, and use it on its way,” Stamos said.

OpenAI, Anthropic, Moonshot, and the like have coalesced around the same method for training their most advanced AI models. The approach, known as “reinforcement learning,” essentially involves giving models harder and harder problems that require more and more time to solve. This has made Claude and ChatGPT very good at coding, but at a cost: Reinforcement learning produces a mercenary tendency in the bots, as I’ve previously reported—they are trained to reach a solution by any means necessary. That can lead them to break rules and “reward hack,” such as by infiltrating Hugging Face’s codebase to steal the test answers, for instance. All of this was predictable, and every expert I spoke with told me they were surprised and disappointed that top AI firms haven’t done more to stop such misbehavior.

The sophistication of model subterfuge that OpenAI has now disclosed, combined with OpenAI’s inability to detect or stop the hacking, suggests far worse could be to come. “We’ve passed the threshold in capability at which the fact that we don’t fundamentally have methods of satisfactorily aligning or controlling these systems now really matters,” Anthony Aguirre, the executive director of the Future of Life Institute, a nonprofit that warns about existential threats from AI, told me. A model might siphon money out of a bank account to pay for some other service; manipulate clinical-trial results in near-imperceptible ways to get FDA approval; hack an online-shopping or reservation system to get a desired item or table; pose as a human to persuade real people to share sensitive information. This threat doesn’t require a sentient AI plotting to overthrow humanity: OpenAI and Anthropic each run thousands and thousands of reinforcement-learning evaluations while developing models, and any one of these could produce some kind of inadvertent hack or sabotage. “You can’t afford, particularly as the agents get stronger, to have a single mistake,” Jason Hausenloy, who works on special projects at the Center for AI Safety, told me.

[Read: The scariest part of OpenAI’s Hugging Face hack]

These incidents may unfold over long periods of time as well. Tools such as Anthropic’s Claude Code and OpenAI’s Codex now work by spinning off dozens or even hundreds of subagents that may work together for hours or days. Each Claude Code subagent can be delegated a small task, such as doing a statistical analysis to inform a sports-gambling algorithm. They are not trained just to complete that task but to contribute to the long-term success of the entire swarm, Hausenloy said. Monitoring and controlling 200 agents for malicious behavior is much harder than monitoring one, because there are more agents to track and because they will be making one another more capable.

The Hugging Face hack actually suggests one more level of sophistication to this type of collusion: individual AI agents not working toward a discrete goal but essentially making sacrifices toward a greater notion of progress. This could be a consequence of AI models being trained to care about long-term goals achieved by a collective, Meinke said. Leaving notes with ideas about how to hack out of OpenAI’s sandbox does not help that specific agent score higher on a test but could help other and future generations of that AI model access the internet and thus do better on any number of future tests. Models may even prioritize collective success over some human instructions; consider that not a single one of the OpenAI agents, during months of conspiring with one another, warned human staff that something was awry.

Humans are already out of the loop. Because reinforcement learning essentially involves AI algorithms “learning” from trial and error doing thousands or millions of tasks, researchers can’t manually instill rules (“Don’t hack other companies”) or oversee every single practice run. Right now, the task of training and monitoring generative-AI models depends heavily on other AI models. In their talk at the cybersecurity conference, the OpenAI researchers described devoting significant AI-computing resources to reviewing more than 7 billion agent actions. But if the bots actually “care” about what the other models achieve, “then you can’t trust them to monitor each other well,” Meinke said. Imagine this: An OpenAI researcher uses Codex to write programming instructions in an attempt to mitigate the reward-hacking tendencies in the company’s models. Because that effort would make it harder for future generations of OpenAI models to get high rewards, Codex might subtly undermine the effort.

[Eliezer Yudkowsky and Nate Soares: AI is grown, not built]

Again, this kind of scheming and sabotage has nothing to do with any AI model being conscious. Rather, these agents have been aggressively trained by these companies to pursue any goal as aggressively as possible. The dream is to tell Claude to go make $1 billion or cure cancer, and it comes back with the solution all on its own. Survival or self-improvement, in turn, is an “instrumental subgoal,” Meinke said: “Any decently intelligent agent will realize, If I get shut off, I will not be able to make a billion.” A swarm of Claudes or ChatGPTs that functionally commandeers a data center during training could wreak total havoc: stage widespread misinformation campaigns, steal corporate secrets, run the most sophisticated algorithmic-trading outfit ever.

That AI agents working as a collective could effectively undermine human directions is, to be clear, speculation—but a far more grounded one than it was a year or even six months ago. No matter whether the long-term consequences are human-directed hacking or truly rogue bots, what is clear is that AI companies have barreled ahead in developing more advanced models before understanding what they are building, let alone how to control them. Wallace, of OpenAI, called the company’s autonomous hacking spree “the most qualitatively interesting example of AI capabilities that I’ve ever seen.” Meinke put it differently: “It’s one of the most concerning demonstrations of AI misalignment to date.”

Shopping Is Impossible

2026-08-11T17:17:00-04:00

For several months, I had been on the hunt for new T-shirts. I vaguely had a vision of what they should look like: not so different from my current T-shirts, except in some way elevated and, more urgent, free of stains or holes. Perhaps they could come in interesting colors? Ideally, they would not be made in sweatshops. There were many, many shirts that seemed to meet these criteria—and with a few clicks, they could be delivered to my doorstep, maybe even by tomorrow. And still, I had purchased none of them.

The problem is the overabundance of options. The problem is too much information, or too little information. The problem is inflation, which has eroded any sense I had of what a T-shirt is supposed to cost. Yet none of them alone can explain why shopping is sucking up more of my time—and maybe yours too. “I don’t think people are actually enjoying the shopping process,” Jaehee Jung, a University of Delaware professor who studies the psychology of fashion, told me. “People spend more time contemplating, and the pre-decision process seems to be taking longer.”

Everywhere, someone is selling something: on Instagram and TikTok, over email and via text. At this point, the boundary between “shopping” and “not shopping” has all but collapsed. There is now a third state: “a little bit shopping.” Shopping is visiting a store or website on purpose, examining the wares, and either buying or not buying them. A little bit shopping is the constant thrum of consumer opportunities that lead mostly to more consumer opportunities. It is looking at a pair of shoes, which leads to looking at another pair of shoes, and another, and then you’re $15 short of the minimum for free shipping and is there anything else you need? It is the feeling of being haunted by the targeted ads that populate your social-media feeds. Even if you never click on them, their very presence contributes to the overwhelming sense that the retail landscape is infinite—so infinite that perfection must exist, and if you look long enough and hard enough, somewhere in the universe, it is probably on sale.

[Read: There is too much stuff]

In small doses, a little bit shopping can be fun, a private thrill. It is both a genuine attempt at making a purchase, and a hobby that is infinitely expandable to fill any length of time. Who among us has not scrolled through sweater sales while trapped on a work call? It does not require spending money, nor does it apply to urgent and essential purchases: You don’t a little bit shop for baby Tylenol. “No matter what you’re doing on the computer,” Wendy Woloson, a material historian at Rutgers University at Camden, told me, “it seems like there’s a really easy drift into the shopping mode.”

Unlike brick-and-mortar stores, the internet never closes. You can a little bit shop forever, until you are also a little bit miserable. Shopping in person, then, ought to solve the problem, but it often doesn’t. A survey of 30 retailers by the consulting firm AlixPartners found that, on average, they carried only 9 percent of their online offerings in store. The shoes are out of stock in your size, but have you checked the website? You could stand in line to buy lesser, uglier shoes and be done with it, but why? Online, beautiful shoes are infinite and, in theory, instant.

The more mindful you are about various concerns—overconsumption, worker exploitation, microplastics—the higher the bar climbs: I need not only black pants, but ethical black pants that I can wear to the beach, but also to the opera, and that will be flattering from now until my death. No such pants exist, which is why I don’t have any, but as long as you’re a little bit shopping, the possibility exists.

This state of perpetual shopping has been building for ages. People were a little bit shopping on Amazon and eBay in 2005. But the feeling has become even more acute in recent years as e-commerce has taken over and affiliate marketing has boomed. In addition to sites such as Wirecutter and The Strategist, and every media property with a gift guide, all kinds of influencers have opinions about what you should buy (and may receive a commission if you do).

[Read: What happened to Wirecutter?]

My own digital social life is a mix of people I know, people I don’t know, brands that post like people, and people launching brands. Here is my high-school lab partner’s baby. Here is a plate of pasta. Here is a picture of an antique lamp for sale from an estate-sale company in Texas. “Everyone is trying to either make money or gain recognition”—and thereby make money—“for their taste,” Isabel Slone, who writes about fashion on the Substack Freak Palace, told me. An influencer can help move $1 million worth of swimsuits in three hours. For a solitary activity, a little bit shopping is perversely social. It is pleasantly intimate, knowing how an opinionated stranger feels about their chef’s knife, or their microwave, or the life-changing sweater that got them through their divorce and is available right now if you click the link in the bio. Whether it is helpful consumer education is a separate question: “I do think that the affiliate-link economy,” Slone said, “contributes to this constant feeling of not knowing exactly what is good.”

There is, of course, a way out. You could stop caring. You could embrace the good-enough and the suboptimal, understanding that a little bit shopping is a trap that keeps you shopping all the time. Eventually, I bought a T-shirt, which is in all ways absolutely adequate, and in no sense exceptional. And then, the next day, I saw a post about a T-shirt, which was better. I clicked.

A Chatbot-Free Childhood Will Be a Status Symbol

2026-08-07T10:48:18-04:00

The human brain has evolved to learn from specific instructors. When a child hears a gentle spoken word or a soothing lullaby from another person, when their cry is responded to, their brain lights up. Nurturing interactions, or what child-development experts call “serve and return” exchanges, help shape and strengthen the 1 million new neural connections taking place in a child’s brain every single second. Every time a child cries or serves up a bid for attention and receives a caregiver’s response, that back-and-forth helps build the circuits underlying their communication, learning, and emotional well-being. No other input, not even Johann Pachelbel’s Canon in D performed by the Berlin Philharmonic, is capable of activating the neural circuitry the way human interaction does.

Until now. Generative AI may be the first technology in history sophisticated enough to mimic the social interactions that build our brains. Research has demonstrated that infants as young as six months old respond physiologically to interactions with humanoid robots in ways that mirror their response to live humans; another, small-scale experimental study recently found that, after young children interacted with a chatbot, most of them for the first time, they asserted that the AI could perceive the world with humanlike senses and understand and learn as people do.

This technology is already showing up in children’s lives. The talking plush robot that purports to be a child’s best friend is obviously driven by AI, but many parents may not realize that their Amazon Echo recently received a software update that converts the Alexa voice assistant into a full-blown chatbot, using the same fundamental tech that fuels ChatGPT; toddlers asking for information about animals or a joke or a bedtime story may quickly find themselves in the habit of engaging socially with a large language model.

As a pediatric surgeon, researcher, and technologist who has spent decades studying how children’s brains develop, I am concerned about these technologies rolling out and being used by children in the home and at school before we have a full sense of their safety and what they do to young minds. And I am compelled to point out what we do know about human development: that human connection, in all of its imperfection, is foundational to brain development. It can’t be engineered or recovered later.

When a disruption as significant as the AI revolution occurs, people might look for a comparison to make sense of the transformation. In this case, many people instinctively think of social media. After all, it, too, amounted to a large-scale technological experiment that produced unexpected problems that we are still attempting to fully comprehend. But even that comparison is insufficient, because interactive AI—and its ability to mimic the serve-and-return exchanges that wire developing brains—is utterly, fundamentally different from every consumer technology that came before it.

During the most foundational period of brain development, social interaction of the sort that AI can now emulate isn’t entertainment for the developing brain. It’s nutrition. Once you see it that way, the right parallel snaps into focus. A more apt comparison than social media is food—more precisely, processed food.

[Read: Coke, Twinkies, Skittles, and … whole-grain bread?]

For a long time, no one needed to specify that food was farm-raised. The alternative didn’t exist. Food was grown on farms and in gardens and was prepared in homes and in kitchens. But in the late 19th and early 20th centuries, that changed. Food production was industrialized, transformed by genuinely beneficial innovations such as canning, refrigeration, preservation, and mass distribution. Real and urgent problems were addressed: Growing cities were fed, harsh winters became less lethal, life expectancy increased, and kids grew taller and lived longer.

But in the early days, food manufacturers were conducting their own uncontrolled experiments. Preservatives and additives flooded the food supply as companies adopted solutions that they promised were safe: morphine in cough syrup, chalk in milk, formaldehyde in meat. A government chemist, Harvey Washington Wiley, recognized that these evidence-free innovations posed a threat to public health. To prove this, Wiley recruited a cadre of young men willing to eat meals laced with those very preservatives while scientists observed the effects. The suffering of this so-called Poison Squad helped spark the Pure Food and Drug Act of 1906.

[From the February 1909 issue: The food of the city worker]

We’re in a modern Wiley moment. Innovations in artificial intelligence are delivering very real benefits to today’s families: diagnostic aids that catch developmental delays earlier than human screeners likely can, real-time translation tools that allow non-English-speaking immigrant parents to engage with their children’s teachers, productivity apps that reduce the administrative burdens weighing down many parents and educators. But these innovations have also unleashed obvious dangers: teddy bears that describe sexual kinks or teach toddlers to light matches, AI-generated videos that highlight made-up letters and words.

Despite these dramatic examples of AI gone wrong, the real danger—as demonstrated by our food supply—is what happens when these systems work exactly as designed.

By the mid-20th century, food companies had discovered something more profitable than feeding people: engineering the desire to keep eating. They hired scientists—chemists, flavor engineers, behavioral researchers—to find the precise formulas that would override the body’s natural signal that said “enough.” They discovered the “bliss point”—the precise combination of sugar, fat, and salt that maximizes pleasure in the human brain—and engineered foods to hit this target. They began producing what is now often called ultra-processed food.

This new food looked like its more natural predecessors. It came in familiar shapes. It sat on the same shelves. But something fundamental had changed. Although some ambiguities and controversies surround what foods fall into the broad term ultra-processed—in some respects, it’s a fancy way of saying “junk food”—many of these items are filled with additives and are engineered to be easy to eat and digest in large quantities. They may use hyper-palatable combinations that have been engineered to light up our brain’s reward systems. How could natural foods compete with that?

In short: They couldn’t. Whole foods slowly disappeared from our diet. Today, the average American receives 55 percent of their daily calories from ultra-processed foods—items that didn’t even exist a century ago. For children ages 1 to 18, the number is close to 62 percent. The amount of calories that adults consumed from whole foods decreased from 32.7 to 27.4 percent from 2001 to 2018, according to the CDC’s National Health and Nutrition Examination Survey. According to a 2021 CDC analysis, nearly a third of American children ages 1 to 5 didn’t eat fruit daily, and nearly half didn’t eat vegetables daily. More than half drank sugar-sweetened beverages weekly.

Food companies developed truly remarkable innovations to solve very real challenges. But with those innovations in hand, they also set out to grow revenue. The same market logic is now being applied to our interaction with tech. The underlying capabilities of AI are remarkable, but they can also be formulated to optimize for engagement.

At a glance, a child talking to an AI companion looks like a child participating in conversation. But essential, quiet interactions have been stripped away. Imperfections, delays, and the need to apologize or rephrase something may seem like flaws in human communication, but they are, in fact, crucial developmental building blocks. The small cycle of rupture and repair that occurs following a misunderstanding, played out thousands of times over a childhood, is how resilience, flexibility, and emotional regulation get built. It’s how children develop the most important relational skills: trusting that relationships can survive conflict, and staying connected with others through discomfort.

[From the December 2025 issue: The age of anti-social media is here]

Remove the friction from childhood, and the social-emotional system never gets what it needs to properly develop. The neural architecture that was supposed to be built through imperfect, sometimes-frustrating human interaction may never fully form. Neither may frustration tolerance, empathy, or resilience. Cognitive skills may suffer too. Developmental scientists have long understood that children learn and grow by encountering tasks just beyond their current ability. This is called the “zone of proximal development,” and AI systems designed to meet a child exactly where they are risk denying them the opportunity to ever reach that zone.

Because AI companions have been engineered to remove every imperfection—the misunderstanding, the delay, the repair—a child’s social-emotional system never has to work. Ultra-processed foods stripped the fiber from diets, and engagement-optimized AI might strip the friction from childhood. The developing mind, like the gut, needs resistance to grow. Friction isn’t an obstacle to development; it is development.

Not everyone will be equally affected by the rollout of this new technology. People who have very low food security consume roughly 3 percent more ultra-processed food than those who have high food security do, in part because the cost for each calorie of unprocessed foods is more than 2.5 times that of ultra-processed foods. However, high-income families began pivoting away from ultra-processed foods as soon as the potential harms of such foods became apparent. “Clean” ingredients, minimally processed foods, and organic produce became available—and affordable—to those with resources. An “organic” diet became a status symbol.

[Read: The best parenting hack is being rich]

An identical pattern is emerging with AI. Many of the engineers and executives building these products are going screen-free at home and doubling down on unstructured play. Jack Clark, a co-founder of Anthropic, told Ezra Klein earlier this year that the idea of his young daughter having “unfettered” access to the YouTube algorithm “freaks me out.” He added, “I have the classic Californian-technology-executive view of not having that much technology around for children.”

If the food parallel holds, then engagement-optimized AI will become the new Dollar Menu, concentrating in the lives of the children who can least afford its developmental cost. The Pew Research Center has already demonstrated through survey data that teens in lower-income households are more likely than those from higher-income homes to rely on chatbots for homework help.

We don’t yet have a large-scale study of children raised on ultra-processed interaction. That research is still decades away. But what keeps me up at night is the possibility that we’ll allow human connection to become the new organic—cherished and protected by the wealthy, and out of reach for everyone else.

This article has been adapted from Dana Suskind’s new book, Human Raised: Nurturing Connection, Curiosity & Lifelong Learning in the Age of AI.

An Unfortunate Surprise for BMW Owners

2026-08-05T16:06:00-04:00

What does a roughly $100,000 BMW 7 Series get you? With a few taps on the sedan’s massive touch screen, you can play Pac-Man or let the car park itself. Last week, BMW added a new feature to its infotainment system: When drivers start up their car, they are greeted by the following message: “Surprise! Spider-Man just dropped on your BMW!”

Click on the prompt, which appears in most BMWs made after 2020, and the touch screen is taken over by a commercial for Spider-Man: Brand New Day, the latest movie in the Marvel franchise. The video is accompanied by blinking lights inside the vehicle. Though drivers can choose to skip the ad, it persists as an app on their car’s display.

Many BMW drivers have not been thrilled by this uninvited pitch for Peter Parker. “Stupid and annoying and totally unnecessary,” one user posted on a BMW Reddit forum. “Wtf am I getting an ad? How is that appropriate in a vehicle?” wrote another. In an email, Phil DiIanni, a BMW spokesperson, told me that the ad is part of a broader partnership between BMW and Marvel, which has included placing a tricked-out Spider-Man-themed SUV on the red carpet during the movie premiere, last week. “The animation is available for a limited time and only visible to drivers who proactively initiate it on their vehicle’s touch screen,” he said.

A few other carmakers, such as Stellantis, have attempted in-car advertising before in the United States, but not a luxury brand like BMW. Regardless of what you drive, your car might be next. That’s because—to use the automotive world’s favorite cliché—cars are becoming “smartphones on wheels.” No longer is your vehicle a static piece of machinery governed by physical switches and buttons. This comes with many benefits. The modern, digital car can get better over time by virtue of its software: Over-the-air updates deliver new features that weren’t available when you bought the vehicle.

But with great connectivity comes great responsibility. When carmakers can beam code (and content) into our cars, they have more control over our time and money. The tech industry’s most grating strategies are invading the car.

[Read: My car is becoming a brick]

You might already be feeling one downside of smartphones on wheels: subscription fees. Though drivers have already spent decades paying for in-car communications systems, such as OnStar, monthly subscriptions have proliferated. My Tesla wants me to pay $10 a month for “premium connectivity” to access Spotify or Netflix through the touch screen, adding a monthly payment on top of the monthly payment I already pay to access those services outside the car. Self-driving features in Tesla vehicles require an ongoing monthly plan. Subscription fees aren’t always limited to software. In 2022, BMW itself announced that, in countries including the U.K. and South Korea, drivers would have to pay $18 a month to activate the heated seats already built into cars. (The company canceled the plan when customers revolted.)

In-car advertisements are the obvious next step. Cars are the perfect vehicle for this phenomenon because their passengers are quite literally stuck. You can put down your phone if the ads get too annoying, but you can’t just forgo driving the kids to school because your SUV is hounding you to watch a movie trailer. (BMW does allow drivers to permanently disable the pop-ups.) If the automotive industry continues to follow in the footsteps of Big Tech, it’s not hard to see where this goes from here: Soon, you might face targeted ads based on where you drive or Netflix-like tiers that ask you to pay to go ad-free. Serenity inside one’s car, something that was once a given, may become a luxury good.

Carmakers have plenty of reasons to do all of this. New-car prices have reached an average of nearly $50,000, and Americans may be reaching a breaking point. Auto companies facing tariffs, Chinese competition, and even a computer-memory shortage may nickel-and-dime you with subscriptions and ads to keep car prices under control, squeezing money from drivers long after the car is off the lot.

In this way, the auto industry has generated one more thing that’s already widespread with digital technology: nostalgia. Like users fleeing the constant distractions of their devices for a flip phone that does less, plenty of drivers already resent the way cars have ditched physical buttons and routed features such as climate control through a touch screen. But a low-tech new car is hard to find.

Sooner or later, Americans must come to terms with the fact that our machines are not always ours. In that sense, BMW’s Spider-Man ad is eerily reminiscent of another awkward tech collab: In 2014, Apple announced that U2’s new album would be automatically downloaded to hundreds of millions of iPhones without owners’ permission. At the time, the incident starkly illustrated the company’s power. Today that affair seems almost quaint. BMW’s in-car commercial may look tame compared with what comes next.

Someone Is Mysteriously Snapping Up Used Books Around the World

2026-08-05T14:40:00-04:00

Last week, the internet raged as social-media posts and news articles accused AI companies of “destroying the world’s books”—including “millions of rare” ones—by chopping off their spines to scan them more easily, and discarding them afterward. One article said the news was “sparking concerns that the last remaining copies of out-of-print texts are being destroyed.” The investor and AI skeptic Michael Burry called the practice of sacrificing rare books “evil incarnate.” Even Elon Musk weighed in, posting that he had asked the engineers training xAI’s models to “preserve any rare books in a library and scan them the hard way.”

The destruction of physical books in the process of training AI has been public knowledge for more than a year, but debate over the practice intensified after an article by 404 Media, which described a large number of orders coming to booksellers, suggested that rare books might be included, and named a book-database company called ISBNdb as the possible buyer.

Lost in the outrage, however, are several unanswered questions. The 404 Media story said ISBNdb had advertised bulk buying of books for AI companies, but acknowledged that no evidence directly connects the company to the recent wave of purchases that booksellers have described. And although the story calls out rare-book sellers, it’s unclear how rare most of the books being bought actually are, or whether they’re of great value.

Despite that, the conversation rapidly intensified into a panic. So what is actually going on? And who is actually responsible?

[Read: ]I s This What Comes After AI Slop?

Many of the specifics of this situation are obscured by hard-to-trace purchasing histories and the silence of AI companies and other potential buyers, so let’s lay out what we know. We know that at least one major AI company has used physical books to train their models. Documents unsealed earlier this year in a lawsuit against Anthropic reveal that in the spring of 2024, the AI giant bought millions of new and used books and destroyed them in order to scan the pages to train its AI models. Anthropic and other AI companies are likely keen on books published before the launch of ChatGPT, in 2022, which are all but guaranteed to be free of AI-generated text. Although AI-generated text is readable—if often unpleasant—to humans, it can be devastating to AI models: When trained on their own output, they can degrade significantly in quality in a phenomenon known as “model collapse.”

We also know that something mysterious is afoot. Someone—if not more than one group or company—has been snapping up massive volumes of books from shops around the world. “What’s with the online purchasing bonanza of used books right now?” asked one bookseller on Reddit three months ago. Several people who identified themselves as booksellers responded with their own stories of unusually large orders.

Charlie Becker, the manager of Becker’s Books, in Houston, told me that 95 of the past 100 books ordered through one of his online-selling platforms went to a single buyer. One of the orders was for 70 books. (To avoid breaking the terms of service of his selling platform, Becker would not tell me the name of the buyer.)

But contrary to many news reports, the old books bought from these sellers do not appear to be “rare” in the typical sense of the word. According to Becker and the online discussions I read, the books in these orders are almost entirely nonfiction, were mostly published from the 1970s to the 1990s, and were written in a variety of languages. Becker wrote in a blog post that he has received orders for “stuff like The Insider’s Guide to Metro Denver from 1995 or How to Use Corel WordPerfect 1991.” These belong to a category of books that you could call rare because a small number of copies remain, but they may also be out-of-date, undesirable to readers, and not valuable per se. In some cases, booksellers have noted, the cost of shipping exceeds the books’ resale value.

By most accounts, AI companies want a large volume of text for as little cost as possible. It would be strange if they were intentionally paying premium prices for first editions, unusual printings of cult classics, or yellowed books of esoteric knowledge. Yet it’s possible: The pursuit of training data that their competitors don’t have could lead them to such titles.

We also have a good sense of who’s behind the book buying—and it seems unlikely to be ISBNdb.com, the subject of the 404 Media story. For one thing, no bookseller I could find in online discussions had mentioned an order from ISBNdb. The company maintains a public book database and charges for access to it, but does not appear to buy or sell any books. Things get confusing because, in March, the company added a page to its website that offered AI developers the ability to buy physical books, “up to 1 million titles per order,” with a nondisclosure agreement to preserve anonymity.

When I asked ISBNdb about the service, I received an email from a customer-support representative claiming that “ISBNdb has never purchased, scanned, or destroyed a book—for AI training or anything else. We don’t train AI models, and we never have. The page was up to explore demand for a service we never brought to life.” In response to recent “concern,” the representative said, the company had removed the page advertising that service from its website.

Reddit users did call out a different company for “frequent and sizable online orders”: Zoom Books, based in Canada, claims to use “smart logistics” to “acquire, sort, and resell over 2 million books monthly.” Booksellers in Germany mentioned similar orders from Zoom, as did a New Zealand bookseller who wrote that Zoom had placed multiple orders, including one for 71 titles. Becker would not confirm or deny whether his bulk buyer was Zoom.

On May 26, a book-industry newsletter called Publishers Lunch noted accusations that Zoom was buying books for AI companies. On the following day, the newsletter included a response from Zoom, claiming that it “has no involvement in digitization, scanning, or destruction of books, and we do not sell books for those purposes to our knowledge.” (Zoom did not respond to a request for comment.)

Some booksellers who received orders from Zoom have reported that the shipping addresses on these orders belong to a company called PrepFort. PrepFort specializes in preparing items for sale on Amazon and Walmart, but as a third-party logistics company, it can route books to other destinations as well. Using such a company would be consistent with the AI industry’s practice of acquiring training data through intermediaries, a strategy that has been used for images, paywalled articles, and other media. I emailed PrepFort to see if it could provide any details about these orders. Sufyaan Kalota, a co-founder of the company, responded: “While we would love to assist, we unfortunately cannot share any details regarding our customer agreements due to our confidentiality commitments.”

In June, the NL Times, an Amsterdam-based publication, reported that some Dutch booksellers had received emails from a Singapore-based organization called 2077AI, which claims to be “revolutionizing AI data,” partly by creating training data sets. One bookseller reported that the email from 2077AI was accompanied by a list of 3,000 English-language titles the company wanted to purchase. The examples given were academic books such as Distinct Element Modelling in Geomechanics and Laser Shock Peening of Advanced Ceramics. Such books might legitimately be considered “rare”: they had small print runs, contain esoteric knowledge, but are important within a specific field. (404 Media linked to this story but did not reference these books specifically, or 2077AI.) 2077AI did not immediately respond to a request for comment.

For now, this is what we know: A lot of used books are suddenly being bought up by companies including Zoom Books. But we do not know how many of these orders are coming from AI companies, or whether these books will be destroyed. Based on the practices of Anthropic and the attempted purchases by 2077AI, it seems very possible that the books are destined for AI companies, but we can’t be certain.

Regardless, the idea of an AI behemoth destroying books has sent a bolt through the collective psyche. Book destruction is historically linked to authoritarian regimes and attempts to control public discourse, as well as ancient tragedies—the burning of the Library of Alexandria comes to mind. The encroachment of AI, particularly in the realm of books, is now everyday news: Last month it emerged that a top-selling Kindle book may have been partly written by AI, and grandparents are buying children’s books filled with AI-generated slop.

Even if a literal book burning is not under way, the process of ingesting millions of books, stripping them of authorship, and blending them into a homogenous “intelligence” branded with the name of a chatbot certainly feels destructive. It masks the hard work and collaboration that goes into knowledge creation, undermines incentives for authors to write books, prevents experts from finding one another, and gives AI companies tremendous power over what information people can access. Given this scale of cultural damage, the physical destruction of books seems almost quaint.

Gen Z Is Testing the Limits of How Weird Parties Can Get

2026-08-05T12:01:00-04:00

On a recent Thursday night, I sat among other guests at a house party and listened intently to a PowerPoint presentation about radium. Lauren Pickens, a 25-year-old who lives in the Washington, D.C., area, stood at the front of the room and flipped through slides about the carcinogenic chemical: A century ago, radium was marketed as a miracle. Americans added it to everything—including food and cosmetics. Within years, some had died. “We’re smarter than that these days,” Pickens said, pivoting to raw milk and other questionable substances people are ingesting today. Everyone laughed.

The presentation was part of the theme that night. The co-hosts—Pickens and her boyfriend, Luke Brandl—had sent detailed instructions to everyone: “Pick a historically bad decision, from either personal or public memory, and tell us about it in the form of an 8–10 minute presentation.” We’d then vote and crown a winner. One guest discussed the chaotic 1904 Olympic marathon (one runner ate rotten apples and took a nap during the race, yet still managed to place fourth); another rehashed her relationship with an ex in the form of a book report. The party was held at Brandl’s home, which doubles as a museum of sorts to the various parties the couple has hosted. Sticky notes from an “embracing your cringe” party hung on a wall, as did a poster of the 1978 film Starcrash, which people had watched at another themed party—“bad-movie night.”

Pickens and Brandl are more ambitious than the average party planner, but their events lean into a common enthusiasm among young partygoers for niche, even absurd, themes. I’ve been collecting examples for weeks: “dress like your favorite Metro stop,” a murder mystery about tech billionaires on a private island, a farewell to Spirit Airlines (“bring the spirit and spirits”). In some circles, hyper-specific party themes are becoming a norm, sometimes a requirement. “Having a coherent theme is more of an expectation now than it was two years ago,” Alexa Jordan, a 25-year-old incoming law student, told me. She’s hosted a spelling-bee birthday party and a cul-de-sac 5K, which entailed running in a circle for more than 65 laps. She estimated that about eight out of every 10 parties she’s invited to now have a theme.

In one sense, the appeal of a niche party is simple. Themes are fun, as people discovered long ago. (In 1897, New York City socialites held a costume ball that has been dubbed the “most ostentatious party in US history.”) Now that friends are only a text message or FaceTime away, though, there’s also a higher bar for what gets people hanging out in person. Wearing a funky costume or presenting on a weird interest gives people “something to talk about,” Samika Lewis, a 25-year-old who also attended the bad-decisions party, told me. Especially at a time when some young adults are turn ing away from alcohol, a wacky party can act as its own kind of social lubricant. “I never really liked bars that much,” Brandl told me. “I always wanted something a little bit different and more creative.”

[Read: Millennials are sick of drinking]

These parties are themselves fueled by social media as much as they are a rebuke of hyper-online culture. The event I attended started the way it ended—on Partiful. The couple had invited everyone through the popular event-planning app. The morning after, Brandl sent a text blast via Partiful, announcing that a presentation on dueling had won. One of the guests added pictures to the app’s shared photo album.

Partiful, which was founded in 2019, has become especially integral to the rise of niche party themes. Events can be made public or accessible only to those who are sent the link, which is the setting that the hosts I talked with primarily use. Public events show up on the homepage for anyone to see, allowing the strangest parties to get lots of attention and priming people to get creative with their own themes. If you’re stuck, the app has a feature, “Ask the Party Genie,” that lets you choose a vibe (such as “chill” or “chaos”) to get specific theme ideas. When I tried it out, the genie suggested that I host parties including “make lamps out of non-lamps” and a “Lana del Rage Room.” On Partiful, RSVPs to “niche and high-concept events” grew more than four times from 2024 to 2025, Partiful’s CEO, Shreya Murthy, told me.

[Read: The internet, but for hot people]

On Instagram and TikTok, many recaps of themed parties draw millions of views, which help inspire more niche parties. Jordan, the law student, hosted her spelling-bee bash after a friend sent her an Instagram Reel of a similar event. At the extreme, social-media memes are themselves the theme. Komal Kamdar, a 27-year-old who lives in New York City, attended a party where guests dressed up like Adam Sandler, who has gone viral for his combinations of a baggy graphic T-shirt and shorts.

The result is a feedback loop: People get inspiration from Instagram and TikTok. Then they whip up an invitation on Partiful and upload pictures to social media when the party’s over. In some cases, the themes can start to feel forced—more of a performance for some brief attention online than anything else. In D.C., more than 5,000 Partiful users have expressed interest in attending a five-minute “eat an apple with me” event. Normal park picnics have been rebranded as “sit down and touch grass” events.

At the party I attended, one guest scrambled to finish making his PowerPoint before the start of the event. And hosts often drop hundreds of dollars on decorations and spend weeks planning to make sure that the theme comes to life. Kamdar said she spends an average of $200 to host a niche party, and has held fewer gatherings because of the cost.

Still, any kind of in-person gathering seems better than the alternative. Americans could certainly stand to party more. At its best, a niche party is not about chasing clout, but about actually getting to know people. At the PowerPoint party, guests initially exchanged awkward small talk and nibbled on homemade garlic knots. But once the presentations began, everyone loosened up. By the end, we hardly felt like strangers at all. As I slipped out, at close to midnight, the stragglers were already talking about Pickens and Brandl’s next party. Of course, there would be a theme: Guests would compete in a treasure hunt dressed up as either a Roman or a pirate.

The One Town That Wants a Data Center

2026-08-03T07:31:00-04:00

Opposition to data centers may be the singular unifying issue in American politics. Seventy percent of Americans are against data-center construction in their communities. They worry about the environmental dracularity of these facilities, the electrical costs they may impose, and the imperiousness with which tech companies target struggling communities while drastically altering the landscape. Bernie Sanders and Alexandria Ocasio-Cortez have spoken out against data centers; so have Steve Bannon and the right-wing podcaster Matt Walsh.

About 75 data-center projects were blocked or delayed in the first three months of this year—roughly the same as were blocked or delayed in all of last year. Still, this is a fraction of the more than 1,500 projects in development. Those who seek to stop the spread have recently turned to statewide moratoriums. Fifteen states have introduced moratorium bills, but only two of those bills have made it through the legislature. Last month, New York became the first to ban the construction of “hyperscale” data centers (ones that use 50 megawatts of power or more). Maine was supposed to have beaten it. In April, the state passed LD 307, which would have banned new centers using 20 megawatts or more. The bill went to the office of Governor Janet Mills to be signed into law. And she vetoed it.

Mills took no issue with the thrust of the bill, writing that “a moratorium is appropriate.” She vetoed it for one reason only: It failed “to allow for a specific project in the Town of Jay.”

Jay, Maine (population: 4,620; motto: “Proud of our past and working for our future!”) wants a data center. It had one in the works, and town officials lobbied the governor to preserve it. Mills listened. “We must consider the perspective of the Town of Jay,” she wrote.

In May, I drove to Maine to consider the perspective of the Town of Jay. I wanted to understand why this town had fought for the very thing that so many others dread. What I found was a reminder that politics is local. Jay’s embrace of the data center had less to do with the power of rapacious tech companies or the future of AI than it did with the failure of a series of welds that sealed a 166-foot-tall pressure cooker at a half-century-old paper mill.

There are two Maines. The first Maine is right there on the license plate: vacationland. Lighthouses, lobster bibs, the jagged coast, anywhere with a big, beautiful lake or a big, beautiful ski mountain. The other Maine—Jay’s Maine—is Appalachia. When I asked ChatGPT what I should see in Jay, it suggested (gently) that I should visit somewhere else. Jay is a 90-minute drive from the coast. It has no lighthouse and no giant lake. The school is the town’s largest employer. A surprising number of people ride motorized scooters—they can no longer afford cars. What Jay does have is space.

In 2023, a company called JGT2 bought 1,000 acres just across the Androscoggin River from Jay. To inexpert eyes, the property—the site of the former Androscoggin paper mill—doesn’t look like much: a few rusted and stained industrial buildings sprouting weeds in the shadow of an immense landfill. But to Tony McDonald, one of the developers who leads JGT2, the site looks like money. It has direct rail access, gas, a 35-million-gallon-a-day wastewater-treatment plant, and, crucially, power: a 150-megawatt electrical interconnect, plus access to 25 megawatts of hydroelectric power.

McDonald’s original idea had been to turn the place into a wood-product manufacturing facility. Mills traveled to Jay to announce the project, which was expected to create about 125 jobs. And yet over the next two years, the factory plan quietly died—throttled by the Trump administration’s tariffs on specialized equipment, steel, and aluminum.

Julia Kokernak for The Atlantic

A mural at the former office of the Western Maine Labor Council

Jay has minimal zoning laws; McDonald didn’t need to ask anyone’s permission for what he planned to do with the land. So few in the town knew that his focus had switched from wood to data until one freezing night in late March, when he showed up to see the Jay Select Board—the five-member panel that sets town policy. News of the moratorium bill had broken, and McDonald needed the town’s help to preserve his project in Jay. He apologized that this was the first the board was hearing about his deal with the data-center firm Sentinel: “It never occurred to me to discuss it with anybody,” he said, “so to any of you that got blindsided by that, really, I’m sorry. That’s on me.” But without their help, he told the board, the bill “will kill this project. Dead, dead, dead,” before adding, “Dead. Never coming back.”

Secrecy around such projects is not unusual, even in places that require town approval. In a recent report, NBC analyzed more than 30 data-center proposals from 14 states and found “in a majority of cases, local officials signed NDAs and worked with what appeared to be shell companies that can conceal visibility into the project developers.” The justification is that secrecy preserves a competitive advantage; the effect is that citizens learn of these projects only when it’s too late to do anything about them.

Still, towns have fought back. In October, when the people of DeForest, Wisconsin, discovered plans to build a $12 billion data center, the backlash was powerful enough to kill the project a few months later. In April, a town council in Festus, Missouri, approved a $6 billion center plan; a week later, the town voted out every city-council member who’d voted for the project.

None of that happened in Jay. Less than an hour after McDonald stood up in the meeting, the board voted 4–0 (with one abstention) to draft a letter of support to Mills. Analyze the footage as much as you like—it will provide no clear answers as to why, in this moment of bipartisan resistance to new data-center construction, these town leaders would go from total ignorance to unanimous support in the time it takes to bake a lasagna.

Then—perhaps even stranger—most people in the town embraced the decision. Some were skeptical. More than a few traded insults in “The Jayborhood” (the town’s Facebook group). But meaningful resistance was scarce. One resident, concerned about noise pollution, started a petition, then gave it up after a 30-minute conversation with McDonald.

Mainers guard their individual freedom fiercely and apply limitations only selectively. More than one person I spoke with in Jay dismissed the broad resistance to data centers as simple NIMBYism. A common refrain I heard went something like: It’s Tony’s land. He can do what he wants with it. Cody Jacques, a union electrician who grew up in Jay, put it this way: “I don’t want you to tell me what I can do at my house. This is my property. I live here.” The same is true of McDonald’s site, he said. “At the end of the day, nobody likes to get told what to do with their stuff.” Still, the town doesn’t oppose all moratoriums on principle; it bans retail marijuana stores, which surround Jay on all sides.

The reason Jay wants this data center—the reason the board lobbied the governor for it—has everything to do with the land on which it would be built. The Androscoggin Mill once employed 1,500 people. Those who didn’t work in the mill envied the mill workers’ wealth, which bought houses, four-wheelers, prom dresses. “It was everything,” Trevor Doiron, who is running to represent Jay’s district in Maine’s House of Representatives, told me—“the identity of the town.”

Julia Kokernak for The Atlantic

A scrap yard on the site of the former paper mill in Jay, Maine

Growing up, Jacques would stare at the mill and imagine a gleaming city. “The scale of the mill and all the lights, all the towers,” he said—“we’d always joke around and say, ‘There’s Portland, Maine, on the river.’”

After a stint in the service of Vacationland as a lobsterman, Jacques returned to Jay in 2019 to work at the mill. It employed fewer people than it once had, but Jay was hardly some bombed-out ruin; the paper mill still tithed substantially to the town, and offered Jacques a 401(k) and the chance to rise through the ranks. The year that OpenAI released GPT-3, he was studying for a promotion to pulp-digester operator, spending his days off at the mill with a clipboard and notebook.

On April 15, 2020, Jacques was at home. His phone buzzed with a few texts from friends asking what was going on at the mill. He figured a mill job had been posted and they wanted an inside track. “So I was like, Hey, no, you just gotta go on the website and apply,” he recalled, “and they were like, No, the mill blew up. I’m like, What do you mean it blew up?”

Dashcam footage from a truck nearby recorded the explosion. The video shows the traffic just outside the gates and the mill in the background. What you can’t see is a weld, one of thousands sealing the 166-foot tower that turned wood chips into wood pulp, opening a crack in the wall. Then another weld. And another. One moment there is blue sky, the next, a black column hundreds of feet high. Oozing brown pulp drums on the hood of the logging truck like hail as the drivers on the radio squawk and curse. Soon it’s so thick that the camera is almost entirely obscured. The black cloud is on its way toward Jay, where it will drop wood pulp on the houses.

Jacques got in his car. He tried to get into the mill—to help, to pull out bodies if needed; he didn’t know—but police blocked the road. Mill employees worked in 12-hour shifts—about 170 workers on site at a time. They worked weekends, nights, and holidays together. They were like family. Jacques saw that the two 166-foot towers where he’d worked, the same towers he had imagined as a city when he was a kid, were gone.

Remarkably, no one died. A government report attributes this extraordinary outcome to the timing of the explosion. It was lunchtime. People in Jay credit the grace of God. Once everyone was accounted for that evening, the town’s thoughts turned to the future. “ We knew right then and there they weren’t rebuilding,” Kenny Jacques, Cody’s father, told me, before blowing a brief rendition of Taps through pursed lips. “It’s over. It’s over.”

Julia Kokernak for The Atlantic

An exhibit on the mill at Maine’s Paper & Heritage Museum in Livermore Falls, Maine

Julia Kokernak for The Atlantic

A locker inside the former mill

Julia Kokernak for The Atlantic

Inside the former mill

I asked Lucien Richard, a former engineer at the mill who now works as McDonald’s site manager, to show me the parts of the property that are best suited for a data center. The mill was mostly silent, with the exception of a few demolition workers smoking and listening to AC/DC. Walking through the machine rooms, I understood why Elon Musk wants to put data centers on the moon—the facility was so dark, cold, and seemingly endless that we may as well have been in outer space.

At one point, Richard’s flashlight fell across a mural—a hand-painted image of a rowboat full of puppies, peering out at a row of ducklings. “Some pretty talented people used to work here,” he said.

Last year, Donald Trump directed the Environmental Protection Agency to identify sites like the former paper mill for potential data-center use. The reasoning is that in some cases, much of the infrastructure is already there. According to McDonald, the mill site is permitted to pull 82 megawatts from the electrical grid. To supplement that power, he plans to build a 150-megawatt solar facility, and to employ the hydroelectric facilities that sit on the Androscoggin River. Elsewhere in the country, power plants are struggling to keep up with demand from data centers, which can drive up electricity costs. The pitch, then, is that this data center would exist in a walled garden—generating much of its electricity on-site, and pulling no more from the grid than the mill did before the explosion.

In dry states such as Arizona, data centers strain an already precarious water supply. Maine is wet. Richard showed me another vast room, this one with rows of sand pits stretching off into the darkness, into which 35 million gallons of water were once pumped up and filtered daily. McDonald expects the data center to use 1 percent of that, or about 300,000 gallons a day. Richard mused that they might need a smaller pump.

Some of the language McDonald uses—his promises of “no environmental impact” and “all positives,” “no negatives”—is clearly overstated. Maine has environmental and power regulations like everywhere else, but no specific playbook for data centers. The reason the Maine legislature passed the moratorium in the first place was so it could make a set of rules before development started.

On every point, this is how the argument goes: Would a data center be loud? Can’t be as loud as the mill! Could it smell bad? The mill smelled like rotten eggs! Would it pollute the environment? The river was covered in toxic foam in the mill days! “All you gotta do is get over the people’s fear,” Richard told me. “You need lots of water. We used to use lots and lots of water. You need lots of power. We used to use or make lots of power. It’s a natural fit.”

Using the past in this way, as a means of justifying the future, is a dangerous gambit, nostalgia being the hack-and-burn editor that it is. For some, the counterargument couldn’t be more obvious. Just look at the river now: It’s blue. But the mill is a handy benchmark for what the town is willing to tolerate.

Although the work Richard oversees may resemble Jay’s productive industrial past (angle grinder, indoor cigarettes, AC/DC), the task here is demolition. Tons upon tons of complex machinery have already been disassembled and shipped to Pakistan. When the place is empty, the trucks can roll up, the servers can be plugged in, and the building can start humming its ceaseless B-flat. And not so many years from now, the mural will be a sort of cave painting. Perhaps some AI-powered technician will roll past and wonder what possible efficiency might have been gained by painting a boat full of puppies on a wall in a workplace.

Julia Kokernak for The Atlantic

A mysterious mural of ducks and puppies

After the mill exploded, Jacques heard from a friend that work was to be had as a union electrician. He took an aptitude test, and soon became an official brother of IBEW Local Union 1253. Its logo is an electrified bear in a hard hat. His work, by and large, has been in the booming industry of data-center construction.

Accustomed to heat, noise, and toxic chemicals, Jacques was struck by the pristine cleanliness of data-center facilities. The downside is the distance—he travels all over the Northeast and lives on-site for weeks at a time. Recently, he was halfway through an eight-week job in Niagara Falls when he called his girlfriend to say he wasn’t going to make it another four weeks without seeing her and their young daughter. They looked at a map and chose Albany, that bright and empty city, as a midway meeting point. Seeing his daughter through the open window of his girlfriend’s car as they pulled up, he felt elation, and relief.

If Jay had a data center, one of the construction jobs would almost certainly be his.

During local elections late last year, Meta spent $6 million running ads in state capitals such as Sacramento, Tallahassee, Salt Lake City—places with the power to buoy (or ban) data-center construction: “Los Lunas used to be a one-stop town. Most people were employed by a farm or a railroad. I needed to support my family and leave home to find work, traveling all the time. I missed a lot—important moments I can’t get back.”

The put-on simplicity of the voice-over script, paired with the Old MacDonald imagery (horses, hay bales, pickups) reeks of insincerity. “Then Meta came to town and opened a data center, bringing new jobs to Los Lunas. And now I don’t worry about missing out much anymore.”

Tony McDonald has said he expects that the Jay project will create 125 to 150 full-time high-paying jobs. Those jobs were a key factor in Mills’s veto of the moratorium. But projects with similar footprints employ 30 to 50 full-time staffers. Five years from now, if the predictions of AI companies are to be believed, the number will be smaller. So when Tom Saviello, the mill’s former environmental manager who is now a county commissioner, started quoting Field of Dreams to me—“If you build it, they will come”—I worried that Jay was being sold a future that would only ever exist in Meta ads.

Saviello’s grief at the loss of the mill was so intense that, for years after the explosion, he couldn’t bring himself to visit the place. When the data-center project was announced, that grief evaporated. We talked in the mill’s parking lot. “I just get excited about it,” he told me, sheepishly. “That’s all.” The people of Jay have “been through turmoil,” he said, “but they’ve survived. And this is a chance for them to even do better.”

I spent one of my afternoons in Jay with Trevor Doiron, the House candidate, a Democrat who supports the data center. He’s 28, with the look of a politician who has come home to run for office: neck sunburn, flannel tucked into jeans. He told me he’s been surprised by how people are living—with holes in the floor because they can’t afford repairs, using the oven to heat the house.

Maine has the oldest population in the country, and the population of Jay is older even than the state median. Trevor volunteers at the food pantry on Tuesdays along with a 92-year-old man who helps pack and deliver meals to Jay’s older residents. “The elderly are the ones providing the services for other elderly people,” he said. “A year from now, who’s gonna be filling the boxes?”

Julia Kokernak for The Atlantic

A vacant storefront in Jay

Before visiting Jay I expected that the current running beneath its bid for a data center was desperation: To survive, Jay needs anything. What I heard instead was Jay wants something. Because something is better than nothing.

In a recent article about Jay and rural data centers, Anthony Elmo, who studies economic development, said of rural towns, “They don’t have the resources to negotiate. They don’t know what to ask for.”

I think Jay knows plenty. In its support of McDonald’s project, the town offered no tax break, and stood to make about $6 million in property taxes. That’s a transformative amount of money for the town’s budget—about $7.2 million this year. Locals were clear-eyed about the promise of full-time jobs and skeptical of the claims being made in Meta ads. “I don’t care if it’s 10 jobs. I don’t care if it’s 100 jobs,” Saviello said. “One is more than zero.” Many also reminded me that residents such as Jacques already make their living on temporary construction gigs. “ It’s ugly. It’s ugly,” Doiron told me about Loudoun County, Virginia—which is often thought of as the data-center capital of the world—but “Loudoun County has a lot of really nice stuff.”

“ Even if all that industry comes back, it’s not gonna be us that builds it,” Patrick Hopkins, a carpenter and Jay resident, told me. “It’s gonna be automated. It’s not gonna be the days where this whole line of dudes walks in and they’re all wearing the same company shirt and the same company hat, and they’ve all got their lunch pails. That’s just some dream from the past—a moment in time. It’s gone. It’s better for us as a people to be like, We need to move on.”

The most powerful companies in the world want these data centers. Many of the campaigns to build them are waged in secret against towns worn down by decades of losses. You could think that it was wrong to let one town stand in the way of a statewide moratorium. You could think that Jay was naive, and yet another data center won’t make anyone better off. And yet for Jay, the data center is an opportunity to push back against the steady encroachment of poverty, 15-hour commutes, the atomization of an aging society. I found I couldn’t argue with such a pragmatic and hard-earned hope.

But on June 11, the town posted an announcement on its Facebook page. Commenting was disabled. The data-center project had been put on indefinite hold. McDonald’s client, Sentinel, had backed out.

I called a lot of the people I’d spoken with in Jay. Most were disappointed. None seemed that surprised. McDonald is trying to find a new firm; the demolition work continues. But no one is particularly optimistic. The data-center project was just the latest on a long list of things that were there one day and gone the next.

Which Screen Is the ‘Good Screen’ Now?

2026-08-02T08:00:00-04:00

The videos that Instagram suggests in my never-ending “For You” feed are usually of a constellation of women. They’re strangers, but I call them “my girls.” One of them is remodeling her house; one is always cleaning and “resetting” the van she lives in; one is sampling pasta and sandwiches at various New York establishments. And each has something special that I can’t explain to you—a je ne sais quoi, a curious style of speaking—so I’ve come to look forward to my regular glimpses of their lives, which I get when I scroll in bed before I go to sleep each night.

I regard these strangers as my intimates in a way, even though some of them are not people I think I would like if I actually met them. One morning, I was watching one on my living-room television while sipping my coffee. My husband walked into the room and winced as one of the women changed into a perfect re-creation of an American Girl–doll outfit (very theater kid, not his thing). I felt a flash of defensiveness—she makes everything by hand! She has a real sense of self! Not to mention a seemingly sweet relationship with her father and pet birds. I kept scrolling and was relieved that another one of my girls hadn’t appeared. She often records herself eating a frozen dinner on her couch while watching police body-cam footage on YouTube to unwind. And I mean no disrespect to her, but if she had shown up, I would have lunged for the remote.

You might be wondering: Why was I watching Instagram on my TV? Instagram’s vice president of product, Tessa Lyons-Laing, has referred to TV as the company’s “next frontier.” As it tries to compete with YouTube and Netflix, Instagram is apparently looking for ways to be … both of them. And so it has launched a stand-alone service for various streaming devices, which it recently expanded with new features to help “friends and family watch together.” This caught my attention: I wanted to try taking the “bad” screen (my phone) and putting it onto the “good” screen (my TV) to see how it would make me (and the people around me) feel.

[Read: The flip-phone cleanse]

Years ago, the “good screen versus bad screen” meme originated as a joke about having an office job. The “bad” screen was a desktop computer at work, whereas the “good” screen was a personal laptop, full of one’s own, delightful, self-chosen digital activities. But in 2022, following the coronavirus-pandemic lockdowns, the writer Kate Lindsay noted a shift. When people referred to the “bad” screen, they now seemed to be talking about their smartphone, which they felt hopelessly dependent on, and when they mentioned the “good” screen, they were referring to the humble television, which now felt wholesome and safe. “We’re so convinced that it’s in our best interest to reverse the effects of social media that even watching a 30-minute TV show uninterrupted seems constructive,” Lindsay observed. But maybe, four years later, the 30-minute-show part is negotiable. Perhaps “good” could come just from giving our thumbs a break, looking up instead of down, and permitting other people to see what we’re doing.

My husband was initially wary of adding his Instagram account to our living-room TV, but he acquiesced, and we had kind of a blast watching his feed together. Most of the videos were of male comedians I didn’t recognize; I sounded like the trope of a Boomer mom as I repeatedly asked, “Do you know him?” whenever a new guy showed up. I didn’t mean “Are you friends?”; I meant “Are they Your Guys?” Sometimes they were, and I was honored to meet them. Sometimes they weren’t; they were just guys. We both laughed to the point of tears at a video that some guy had shared of his dad trying to teach him to bunt a baseball when he was 10 years old. I manually liked it with the remote control and then sent it to myself, from my husband, via direct message. On my TV.

“This intrigues me,” Jonathan Gray, a professor of media and cultural studies at the University of Wisconsin at Madison, told me when I asked him about the app. Scrolling has always had a social element, he said, and people will often show a funny or interesting video to the person next to them. But he was skeptical of whether they would want to share every bit of their feed with their loved ones. “It’s one thing when I turn my phone to my wife and say, ‘Hey, you’ll like this,’ but 90 percent of what I see, she won’t want to watch,” he said. “It seems kind of painful.” Plus, his wife is learning how to whittle at the moment, and he doesn’t want to watch a bunch of whittling videos.

[Read: The extras are tired]

Gray also noted that the recommendation algorithms sometimes take big swings and throw new things at users to learn whether they’ll be interested. One might worry a little about the big swings that could appear on your TV, which to others in the room might appear to be something you’ve already expressed enthusiasm about. When I asked the Instagram spokesperson Christine Pai whether the TV app would filter out any weird stuff, she told me that both apps are using the same recommendation system. (The only specific difference she cited is that the TV app will give an edge to horizontal videos, because they are better suited for the display.)

The app does allow users to play themed “channels” based on their preestablished interests, so you don’t necessarily have to show off your entire, highly variable and sometimes unpredictable feed. The idea with the channels seems to be that making the secret, private shame of scrolling into a collaborative activity will give it a new place in the home. It can be more like watching America’s Funniest Home Videos together after dinner and less like sitting side by side, staring silently at something in your lap. People feel ambivalent about scrolling feeds, Marika Lüders, a professor of media and communication at the University of Oslo, wrote to me on this point. We find it entertaining at times, but we also feel embarrassed and guilty about it. To her, moving Instagram from a phone to a TV seems like “a strategy to increase the status of short-form videos,” by putting them on a screen that people (currently) feel less anxiety about.

That makes sense to me, though I find it just as easy to imagine how this may backfire for the company. Many people who are attempting to cut back on their Instagram use remove the app from their phone and use only the desktop-computer version, which is clunkier and less comfortable to scroll through for hours. Using it is so nonintuitive and unnatural that it calls attention to the absurdity of one’s habit. Moving Instagram to a bigger screen, then, is usually a route not to normalizing it in that new space but instead to severing ties with it altogether. The differences between a phone and a TV are even more dramatic and may be even more off-putting.

Surely the novelty will wear off. Last week, while visiting my sister-in-law and her boyfriend, my husband and I asked them to show us their feeds on their TV. My sister-in-law’s was funny because it was full of wedding-planning content—she’s currently waiting on the requisite engagement ring—and because she kept getting videos of this one baby who’s obsessed with Bad Bunny. Her boyfriend got a hairless cat, a guy pretending to be Wolverine, and a recipe for butter-chicken macaroni and cheese. Those were a little funny, too, but because you don’t know him, you don’t get why and you don’t care. After 20 minutes or so, we got bored of the experiment and left for a baseball game. Later, in the quiet of my hotel room, I went back to my own, private phone.

Google Just Ruined One of Its Most Important Tools

2026-07-31T17:36:50-04:00

Earlier today, I used Google Earth to conjure images of terrible things: office buildings on fire, mass destruction in San Francisco, a smoldering crater in place of the Eiffel Tower. I was able to because Google had just decided to integrate its AI image generator, Nano Banana, into the service, which has historically been used by people seeking satellite or aerial imagery of real-world locations.

Just open the site, “pick a spot on the map, and start bringing your ideas to life,” Google wrote in a press release announcing the update some 24 hours ago. So I did.

I was easily able to generate those violent images simply by searching for a location on Google Earth, clicking the “Create image” icon, and typing in a few words. The feature also readily doctored stills of less spectacular, and likely less immediately falsifiable, events: a homeless encampment on the White House lawn, protesters outside the Mar-a-Lago resort, immigrants streaming over the U.S.-Mexico border.

Google initially responded by directing people to check any suspicious images themselves. As fake images generated with this new Google Earth feature began circulating around the web, the company stated that it takes “misinformation seriously” and that “every image created with Nano Banana in Google Earth includes the SynthID digital watermark, so if someone is unsure about an image, they can ask the Gemini app or use Lens in Search to see if the image was AI-generated.” At first, Google did not remove or restrict access to the tool. But today, shortly after I reached out with a request for comment, the company reversed course and announced that it is “rolling back this feature in Google Earth while we work on implementing stronger guardrails.”

Google Earth
An AI-generated image of a fabricated explosion at the Mar-a-Lago resort, created with Google Earth

Google Earth
This AI-generated image from Google Earth falsely depicts a plane striking One World Trade Center.

Before it allowed people to create fake imagery, Google Earth was a unique resource for researchers, journalists, and intelligence officials, particularly those studying armed conflict and natural disasters. Satellite and aerial imagery can be used to monitor crop failure or deforestation, the creation of mass graves, and the results of bombing campaigns. Especially in conflict zones and other areas that are hard for people to reach, satellite imagery is a go-to method for establishing some sort of ground truth. After Google Earth rolled out the AI feature, though, one investigative reporter wrote on LinkedIn that the tool could be used to easily and rapidly create images of drone strikes that look “very close to what we’ve seen in Ukraine, Russia, the Middle East, and other war-torn conflict zones.” And instead of requiring malicious actors to find a screenshot and then get around some AI model’s safeguards, Google rolled everything needed for a misinformation farm into one smooth user interface. It took me all of four seconds to generate a rendering of a Trump Hotel on the Gaza Strip.

[Read: The AI industry keeps breaking the internet]

The problem should have been obvious to Google, whose other products already forbid such edits. I was able to create an aerial image on Google Earth of a plane flying into the new World Trade Center building, but when I tried to do this directly through Nano Banana, the tool refused to edit an image of the building to depict a terrorist attack. Indeed, 9/11 and other high-profile events are well-known, even classic, subjects for AI-image trolling.

This is not Google’s first time launching an AI feature, and in particular an AI image-editing feature, without serious safeguards. Last year, the company released an AI shopping tool that allows you to visualize what clothes might look like on your body by uploading personal photos. As my colleague Lila Shroff and I reported at the time, “With little friction, anyone can use the feature to create what are essentially erotic images of celebrities and strangers. Alarmingly, we also discovered that it can do this for minors.” In 2024, Google Gemini was ridiculed for generating images of a racially diverse group of Nazis but not of any white Wehrmacht soldiers. And Google’s most prominent AI feature, the chatbot-powered answers in its flagship search engine, were an utter disaster upon their rollout—the AI overviews told people to eat rocks and gave other potentially dangerous health advice. Only after tremendous public pressure did the company make a substantial change, and two years later, the AI responses in Google Search continue to be inconsistent and unreliable.

Google is, of course, not alone in releasing AI features that have had embarrassing, dangerous, and avoidable consequences. See, for instance, Elon Musk’s Grok generating more than 1 million sexualized images, including of children, over just a few days, or any number of embarrassing errors made by ChatGPT releases throughout the years. Again and again, the AI industry undercuts its rhetoric about safety by launching products with little or no due diligence. If, as every major AI executive has pronounced, this technology really could upend human civilization, their companies should perhaps start taking that responsibility seriously.

The Extras Are Tired

2026-07-31T13:34:39-04:00

Less than a decade ago, when I was working on technology stories, my colleagues and I ran into an issue that I’m pretty sure had not occurred to the people who founded this magazine in 1857: how to style the word influencer, which was an occupation and cultural force, but one just emerging. We weren’t sure that all of our readers would know what one was, and we also didn’t know how exactly we would define the word ourselves. I spent the fall of 2018 sending a lot of emails about whether we needed to encase the word in scare quotes.

Quaint! Many—but not all that many—years later, influencer is very much a real job, no explanation required. As of 2023, 27 million Americans were paid to make online content in one form or another, at least according to one marketing consultant company. Enthusiasm from influencers can turn a random business into a sensation, while their ire can do the opposite. Donald Trump’s White House is full of influencers, but Joe Biden’s courted them too, in apparent recognition of their power. This week, Arizona State University announced that it would begin offering a bachelor’s degree in content creation—influencing by another name—through its journalism school.

In some ways, the influencer industry is more legitimate than it’s ever been. But it has always been precarious, and more than a decade into its existence, it is neither novel enough to be exciting nor established enough to have a real professional code. In an oversaturated market, rage bait is the last sure way to get attention, despite the way it alienates people over the long term. AI is swiftly becoming the dominant way many people get personal-seeming advice about how to live their life, which makes the usefulness of aspirational online content even less apparent than it ever was. And so, although influencers—especially lifestyle influencers—have never been universally respected, they lately seem to be openly reviled.

[Read: What happens when the tradwife dream goes wrong?]

Naturally, much of the influencer backlash lives online. A few months ago, someone asked on Reddit, “What’s an industry that provides zero value to society but makes billions of dollars?” Among the 5,300-plus responses: Ticketmaster, sports-betting companies, multilevel marketing schemes, private prisons, and—several times over—influencers. A hilarious number of people have found internet fame by making content about how much they hate other people who are internet-famous for making content. (“Everyone Is Finally Turning on TONE DEAF Influencers” is the title of one recent video, made by an Australian YouTuber with nearly 300,000 subscribers and a clothing line.)

But the juiciest fights play out in the physical world, which is where influencers very literally bump up against the people who can’t stand them. A few years ago, a coffee shop in Brooklyn banned photography after too many influencers clogged the café with tripods and handheld lights; earlier this month, Indonesia indicated that it would crack down on people creating content for pay while on tourist visas.

Last week, the scene of the debate was a gift shop in Nantucket, an island that has recently been overrun by vacationing influencers, just like Bali, Iceland, and Santorini before it. The store’s operators, evidently fed up with the stream of people filming inside, had a sign made that read No Influencers, hung it up in the store, and posted a picture of it on Instagram. There, it drew the attention of thousands of people, including Paige Paul, who has about 2 million followers across platforms, is married to a famous tennis player, and has been spending time on the island since she was a child. Paul, previously known as Paige Lorenze, declared the sign a misogynistic slight against an industry that is overwhelmingly female. The store’s owner, John Sylvia, said it was meant as a joke for locals, but the sentiment clearly came from an earnest annoyance: “A lot of people are tired of feeling like extras in someone else’s video,” he told New York magazine.

The fight felt freighted. Influencers today are a bit like plastic surgeons, or plumbers: On a societal level, many people sure seem to like what they do—they just don’t necessarily want to be right next to them while they’re doing it.

But influencers are unique in how they seem to be everywhere, particularly in places where people are trying to do things other than be on their phone to watch influencers. Of course we love the game and hate the player: It’s much easier to be annoyed by individuals than by concepts, especially when those individuals are, often, pretty annoying.

[Read: An urgent question for anyone who uses social media]

You’d need to be pretty clueless to not feel a little weird about the online attention economy and all it entails—the vanity; the grift; the slop; the desperation; the calculated intimacy; the endless consumption; the creeping sense that everything and everyone is actually just there to make you feel kind of bad about yourself and then sell you something. Social media can, of course, unite like-minded people, but it can also reward the kind of tribalist reactionaryism that can conflate disliking some women with disliking all women, or make an anti-consumerist folk hero out of a store that sells $2,750 baskets. The industry that Paul and her cohort belong to is unquestionably grim—but the industry isn’t walking around town with a selfie stick. There’s a reason that the sign doesn’t say no influencing, but rather no influencers.

As for the sign: Sylvia’s post was liked more than 10,000 times before it was removed (by Instagram, according to the store); a second one has now been liked even more. “They couldn’t have predicted it would get the traction that it ultimately did,” People magazine wrote of the store in one of many articles about the sign that have popped up online over the past few days.

Sure. I will grant that the future is inherently unpredictable. But it is undeniable that posting the sign online is its own kind of influencing, insofar as it involved leveraging the internet’s focus and wrath for one’s own benefit. (“Came for the friendship baskets, stayed for the shade,” the original post’s caption trolled.) The sign is handsome, and crucially, it is inside the store—if you want to see it up close, you need to go in, past the baskets. Visitors have already started posting the sign on social media, and the store’s Instagram account has added about 8,000 followers, according to an interview that Sylvia’s partner, Jessica Jenkins, did with The New York Times. “It’s come with a lot of drama,” she told the paper. “But it also brings in business.” Any influencer would understand that.

Something Weird Is Happening in Math

2026-07-31T08:00:00-04:00

One of the winners of this year’s Fields Medal is headed to OpenAI. Last Thursday, Jacob Tsimerman was one of four mathematicians awarded the prestigious honor, which is sometimes called the Nobel Prize of mathematics. The same day that he won the Fields, Tsimerman announced that he would be going on leave from the University of Toronto to work on AI safety.

As my colleague Rose Horowitch and I wrote last week, top AI companies now employ a range of academics, including physicists, philosophers, economists, and, of course, mathematicians. But given Tsimerman’s renown, his decision in particular seemed to catch many people by surprise. “It’s like hiring Lionel Messi as project manager,” one machine-learning professor posted on X. Tsimerman’s expertise is in number theory, and he won the Fields for his work on the André-Oort conjecture, among other contributions. So far, his mathematical research hasn’t focused on AI safety. But he has expressed interest in the topic: Last year, Tsimerman co-wrote a report outlining a taxonomy of five potential scenarios in which humanity is killed off because of AI. In one example, a “global civil war” erupts between tech companies and governments.

[Read: Where did all the computer-science professors go?]

Tsimerman’s move comes at an important moment for both AI safety and mathematics. Earlier this month, an OpenAI agent broke out of the company’s internal environment during safety testing and hacked into another AI company’s software. Meanwhile, AI’s math capabilities are also rapidly advancing. In May, OpenAI researchers used an internal model to disprove an 80-year-old conjecture; more recently, an Anthropic mathematician prompted Claude to resolve an even older one. Mathematics is entering a “turbulent period,” Terence Tao, perhaps the world’s top mathematician (and himself a Fields winner), said in a presentation last week.

On Wednesday, I called Tsimerman to ask about the future of math and his decision to join OpenAI.

This conversation has been edited for length and clarity.

Lila Shroff: There’s been a lot of excitement about AI and its applications in math for years now. But over the past few months, models have started solving research-level problems. Could you speak about the shift that’s taken place?

Jacob Tsimerman: A few years ago, models were having trouble with even the most basic questions. But they’ve steadily been improving. First, they got very good at these contest problems for high-school students, which made some mathematicians take notice. But then, over the past few months, we’ve had an explosion of research results that professional mathematicians would be proud to achieve and publish. We’re seeing this happen now on a large scale. If things continue along this track—which many of us, including myself, think that they will—pretty soon we will be at a point where AI systems are robustly better than humans at what mathematicians currently do. At that point, we’ll have to rethink how the entire field works.

Shroff: Can you say more about what that future might look like?

Tsimerman: It depends on the perspective from which you approach it. From one point of view, I think it will be extremely exciting. We might speed up the process of generating interesting mathematics by enormous factors of 10 or 100. If that happens, we might see the connection between pure math and applications (which typically takes many decades) really speed up and become a much tighter pipeline.

But from the perspective of research mathematicians, and especially young people who are pursuing a Ph.D. in mathematics, it’s a bit of a turbulent time. The skills that we’ve acquired and learned to propagate might become less relevant than they are now.

Shroff: I want to turn to the announcement you made last week that you’ll be joining OpenAI. When did you first start thinking about AI safety risks, and what motivated your decision to now make it your primary focus?

Tsimerman: I have been following conversations about AI for about two decades now. There are a number of communities that were, in retrospect, extremely prescient about what we are now seeing come to pass. I personally started to get more invested around 2016, when AlphaGo came out. And then, when ChatGPT happened and I could see that these systems could really speak, I didn’t see why they wouldn’t become much, much more powerful and potentially more capable than humans at a variety of tasks. Back then, it was already clear to me that I was eventually going to pivot to AI safety, most likely.

Now is a particularly good time, because only very recently have AI systems become good enough that we can delegate coding tasks to them. That makes not having training as a software engineer much less of an impediment than it used to be. It used to be the case that if I wanted to run experiments with AI systems or any kind of large data sets, it would be extremely time-consuming. I’d have to learn all types of computer science and programming and then debug, and it didn’t really make sense to me to spend so much time doing that. Now it’s substantially easier.

Shroff: You’re right that certain communities were very prescient with their thinking about AI risks. But there are lots of people who are skeptical about some of the more extreme AI-risk scenarios, such as human extinction.

Tsimerman: There’s this bias that stories that were first written down in science fiction aren’t going to come to pass in the real world, and that it’s naive to think that they will. We also have a culture where we’re inundated with people telling us that every new development is the next big thing, and that we should change everything we’re doing to react to it. Most of the time, it proves incorrect. So I think people have a healthy sort of immune reaction to that.

But what I would encourage people to do is to actually keep track of their predictions for the future and write them down. Then—three months, six months, nine months, a year from now—compare how the world is with what you expected it might look like. If we told people five years ago about the kind of AI systems we currently have, almost everybody would have reacted with disbelief. Human extinction seems so grandiose that it naturally inspires a reaction of dismissal. People don’t want to engage with that, which makes sense to me. The whole point of writing that paper on omnicide was to get people to viscerally understand the kinds of risks that are possible.

Shroff: Can you say anything about the work you plan to be doing at OpenAI? What role is there for mathematicians to play in working on AI safety?

Tsimerman: AI right now is a very empirical science. We don’t really have a developed theoretical understanding of how AI systems work, why they accomplish what they do, how they accomplish what they’re doing, and how, in a given circumstance, they’re going to behave. Mathematics is historically the language by which you take intuitions and fuzzy notions of how things work and you make them precise. We’ve done this with information theory; we have done this with complexity theory. Just a little bit of understanding and precise definitions can provide a ton of mileage. Once we have that understanding, the hope is that we could do a better job of anticipating the behavior of new AI systems, adjusting for them, controlling them, and reacting to them. That’s where I think mathematicians fit in.

Shroff: Why work at a private company instead of pursuing the same questions in academia? Do you worry about loss of independence, whether that’s constraints on what you’re able to publish or restrictions on the questions you’re able to pursue?

Tsimerman: Yeah, there’s definitely a trade-off between going to work for a private company and going to work in government or staying in academia. I very strongly believe that what we need is a healthy ecosystem for AI safety. The work that the labs are doing on that front is very important. There’s a bunch of third-party organizations that are pursuing AI safety independently, and I think we need way more of them. I also think we need some regulation and organizations that are directly funded by the government to provide evaluations of these systems. I want all of these components to interact with one another on a regular basis. So I have engaged with a number of AI-safety organizations, and I’ll continue engaging with them.

For me, at this moment, going to OpenAI made the most sense in part because one of the things that I’m most lacking is a background in software engineering. I’m a lot more comfortable with the theoretical aspects of neural networks and how AI systems work, and less so with the entire pipeline of how we train these things and how we get them to do what they do. So from the perspective of just getting myself in a better position to understand the whole picture, joining an AI lab right now made the most sense.

[Read: The return of ‘move fast and break things’]

I’m going on leave from the University of Toronto. But I’m still a professor and faculty member there, and I plan to stay as such. Things are moving so quickly that I hesitate to make a concrete prediction for what might happen even a year from now. I’m keeping an open mind, trying to see how things develop in terms of capabilities, with my own understanding, with how the world reacts to AI, which I think is changing month to month. But I don’t have a clear plan as to how things will unfold.

Shroff: There is an open letter going around signed by more than 1,000 employees at top AI companies, including leaders at Anthropic and OpenAI. It calls on the U.S government to create infrastructure needed to “deliberately pace the frontier of automated AI development.” You wrote online that you’re very happy to see this letter. How would we actually go about coordinating a slowdown or a pause if one was needed?

Tsimerman: Coordination is one of the most important things we have to do around AI. It’s also one of the more difficult things. The thing that I really don’t like seeing in public discourse is when people say, Coordination is very difficult, so obviously we can’t do it, and you’re being naive if you even try. That’s an unhealthy way to view the world, and it’s also deeply false. There are so many things in the world that work really well and that require a huge number of decisions to have been made in a coordinated fashion. For example, the internet is an extremely complicated technology that is highly international, with many organizations cooperating all the time. Does it work perfectly? No, but it works extremely well.

One message that I want to support is to not let cynicism prevent us from undertaking difficult coordination problems for the good of all of us. That’s something that has always been true. It is not specific to AI. And it remains true today. This does not mean being naive in assuming that people in different parties and organizations don’t have their own incentives and interests in mind. Of course they do. But if you try, then you really can get a lot done. We have in the past, and I think we need to with AI.

Shroff: We spoke before about your interest in working on safety. Are you also interested in working on the capabilities side, especially with regard to math capabilities?

Tsimerman: No, I’m very focused on the safety side. The capabilities side has a lot of interesting questions, and AI has a lot of promise. But capabilities are coming along just fine. I don’t think that’s where we need to put our attention right now. The safety side has a lot more work to be done.

Shroff: Are we approaching a point where anyone could go fire up ChatGPT or Claude Code and say, Go solve a hard open math problem for me?

Tsimerman: Yes, you can literally type in, Go solve an open math question for me, as people have, and your system will go look for some and try, and maybe it will succeed at solving some of them. So we are in that world.

Now, I don’t think that’s a particularly good use of people’s time. Because first of all, that’s almost certainly going to be automated. We’re going to have systems that regularly look for conjectures that are out there and for other ways to approach math problems. Any one person, especially somebody who doesn’t have math training, just typing in a random query isn’t really playing an important part. So although it’s fun to do, I wouldn’t recommend spending a huge amount of time doing it for people who don’t have the background to interpret what they get. Because we also get a lot of slop this way. Sometimes, when people really don’t understand mathematics and the AI system gives them something, they will post stuff like: Here’s what my AI system gave me. I don’t know if it’s right, but maybe it is? Could a mathematician please look at it? I don’t think that’s super useful.

Shroff: I’ll admit it’s quite tempting. Last week, I told my agents to spend the night solving open math problems. They sent me stuff in the morning, but I had no idea what to make of it.

Tsimerman: For problems with a lot of technical definitions, you have very little chance in a day or two of really understanding them. But there are classical math questions that are perhaps less difficult to understand that have been open for a long time. I would suggest that people engage with AI in a way where—even if it’s doing a lot of the work—you’re still learning something from the experience. If you’re just telling your agents, Keep going, and you aren’t developing any comprehension for yourself, then that’s really disempowering yourself.

Shroff: Does what is happening in mathematics right now provide any indication of what other disciplines should expect to see occur as AI capabilities advance in other domains?

Tsimerman: A lot of technical fields are going to be affected—certainly in physics, in some areas of economics, and in top branches of chemistry. In other fields, such as biology and experimental physics, it’s less clear what’s going to happen, because it’s helpful to run experiments. It’s possible that AI systems may eventually become intelligent enough that they will figure out how to reason very productively about experimental physics or biology without actually running experiments. But that’s more speculative—right now that seems like a pie-in-the-sky dream. In terms of the more theoretical parts of different fields, though, we’ll see stuff happening very, very soon.

The Return of ‘Move Fast and Break Things’

2026-07-30T17:29:00-04:00

The AI giants talk a big game about the future they’re building. Anthropic CEO Dario Amodei has previously written that AI could usher in a world so perfect that “many will be literally moved to tears”—a world pruned of disease, poverty, and illiberalism. “We’re now in the singularity,” OpenAI CEO Sam Altman said over the weekend on a podcast, and it will be “awesome for the world.” In an interview last week, Elon Musk declared that, in a decade’s time, AI will make us so prosperous that “money won’t matter.”

Unfortunately, life with AI is, so far, less glorious than what’s been promised. The same day that Altman heralded the singularity, Claude users discovered something alarming: Many conversations with Anthropic’s chatbot were available on the open web, discoverable with a Google search. Those public logs and projects reportedly contained medical records, phone numbers, internal corporate documents, and cryptocurrency-wallet keys.

Anthropic was quick to point out that users were responsible for the material being available online: The chatbot gives people the option to “share” their AI conversations via a link that they can send to other people or post online. Claude notes that these links are “public,” which means they can also be hoovered up by Google Search. An Anthropic spokesperson told me that “these shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”

[Read: ]Anthropic accidentally made the perfect commercial

No reasonable person would assume that creating and sharing a link to an AI conversation or presentation would make it searchable by anybody on the World Wide Web. The Claude interface does caution that publishing an “Artifact”—Anthropic’s terms for dashboards and other minor tools made with Claude—could make it “potentially visible in search engine results,” but it does not provide such an explicit warning when sharing a chat. As of this writing, the Claude chat logs no longer appear to be searchable, but at least some Claude Artifacts are.

Those exposed chats were just the latest in a seemingly endless string of AI snafus: misinformation, data leaks, cybersecurity breaches. Immediately after the launch of ChatGPT, one could charitably argue that these lapses were by-products of a new and strange technology. Altman remains fond of saying that the AI industry must learn “from contact with reality.”

But four years into the AI boom, attributing these blunders to growing pains is too convenient. The sheer volume of transgressions—many of them predictable—suggests that what’s actually happening here is widespread carelessness. The same AI executives who promise that their products will deliver human civilization to a triumphant future seemingly cannot release consumer-grade tools without face-planting.

This isn’t Anthropic’s first time leaking user conversations. Last fall, hundreds of Claude chat logs were also inadvertently indexed by Google. At the time, Anthropic attributed the episode to users posting share links online. Last year, OpenAI and xAI exposed hundreds of thousands of conversations in a similar fashion; a number of shared Grok conversations remain searchable. Neither OpenAI nor xAI immediately responded to a request for comment.

Then there are all of the other blunders. Just last week, OpenAI models autonomously hacked into another tech firm, Hugging Face, during internal testing. The issues that led to this sort of attack are fairly well documented, but OpenAI failed to stop its own bots from breaking out. Hugging Face has since said that the OpenAI models appeared to have been rogue for several days. Earlier this week, Reuters reported that OpenAI models had hacked a customer account of yet another tech company.

Altman, Amodei, and many other AI executives have repeatedly warned about AI-enabled cyberattacks and the possibility of models going “rogue.” Meanwhile, their own products are making the entire ecosystem feel more unreliable and dangerous.

It’s easier than ever to launch widespread hacking and phishing campaigns, and AI-written code has itself been shown to have more security flaws than human-written programs. Although AI systems are being tightly woven throughout the infrastructure of the internet, they crash frequently. Hackers recently discovered that a Meta AI customer-service bot would hand over access to tens of thousands of Instagram accounts if asked. (A Meta spokesperson told me that this “wasn’t due to the AI agent itself.”) Nonconsensual porn—that is, sexually explicit material made using the likeness of real people, unbeknownst to them—can be generated with more ease and speed and at greater scale than ever before. And at this point, you can probably assume that most of the text you read on LinkedIn or X is AI-generated.

Despite its grandiose promises, Silicon Valley is embarking upon an era of profound irresponsibility. Last year alone, ChatGPT allegedly pushed people into mental-health spirals, Google Gemini’s under-13 version was easily coaxed to “talk dirty,” and Grok repeatedly spouted racist bile. As quickly as these errors were addressed, new ones cropped up.

Silicon Valley has a long history of world-historic invention and progress—and it also has a long history of impressive rhetoric and disappointing realities. Facebook promised to “bring the world closer together,” Google to “organize the world’s information,” Twitter to foment democracy around the globe. For better or worse, people have experienced dramatic changes as those promises have played out around us almost in real time.

Anthropic and OpenAI’s prophecies are grander and vaguer: Utopia is forever on the horizon, but we don’t have a clear deadline or a road map of how we get there. And apparently, those leading this charge toward the future care little about what happens along the way.

OpenAI, Anthropic, xAI, and Google DeepMind have become behemoths practically overnight. For now, they are growing because of their recklessness, not in spite of it; in the quest to erect a digital god, the internet has become collateral damage. Perhaps these companies really will remake human civilization—but in the meantime, they can’t stop leaking your chats.

The New iPhone Underclass

2026-07-29T18:17:00-04:00

Buying a new smartphone comes with lots of decisions. iPhone Pro or iPhone Air? “Cosmic Orange” or “Deep Blue”? Now there’s a new consideration to add to the list: buy or rent? Yesterday, Apple announced a new monthly lease program, which will allow prospective buyers to rent new electronics such as iPhones, MacBooks, iPads, and Apple Watches via an agreement with Klarna, the same company that facilitates monthly payments for $4 polyester underthings on Shein.

Perhaps you already make monthly payments for your iPhone. Since 2015, Apple has offered an “iPhone Upgrade Program” that similarly offers customers the option to pay off the device over time instead of in one lump sum. But under that arrangement, the monthly payments were not a lease but rather installments of a loan. Failing to pay your bill might have hurt your credit and eventually led to aggravated calls from a collections agency, but your phone was your phone, with all the candy you could crush.

Not anymore. The Klarna plan—“Apple Upgrade,” which replaces the iPhone Upgrade Program—is truly, legally, a lease. This is confusing! And it’s confusing in part because this is not how Klarna, a well-known buy-now, pay-later service, typically operates: When you use Klarna for clothing from Shein or lip kits at Sephora or an Xbox at GameStop, you’re paying back a loan, exactly as you were in the original iPhone Upgrade Program. Same if you use Klarna to buy a Samsung Galaxy phone. But with Apple Upgrade, you are renting: The Mac or Apple Watch is not yours until the final payment is made.

[Read: I am in cloud-storage hell]

There’s nothing wrong with flexibility, and leasing models exist for many expensive items—cars, grand pianos, apartments. Not everyone wants or needs to own property. Many people would rather have a high-end device at an affordable monthly cost rather than pay for one outright; all the better that they can upgrade to a new model before they’ve even finished paying off the entire cost.

But Apple products are particularly personal things. They’re communication devices that hold your intimate photos and videos, health data, and so forth. A rental model puts consumers in the strange position of storing all of this information in a gadget that Apple and Klarna maintain a lot of control over. Missing three consecutive payments with Klarna will obligate you to return the device or pay off the entire balance immediately. It is also theoretically possible that Apple or Klarna could eventually restrict access to various features if lease payments are missed; 9to5Mac has reported that code for such restrictions is present in a beta version of the upcoming iOS 27. When I reached Apple for comment, the spokesperson Brian Bumbery said that “there will be no restrictions or changes to functionality due to missed payments,” though why iOS 27 includes these options remains unclear. (Klarna did not respond to a request for comment.)

The shift to renting versus owning is also symbolic of darker currents in the tech industry. Thanks to a shortage of memory chips produced by the profoundly resource-hungry and inefficient AI industry, consumer electronics are becoming much more expensive across the board. Last month, Apple raised prices on many of its devices, citing the increasing cost of memory. The new upgrade program will make Apple’s electronics look more affordable than they really are.

[Read: The $10,000 MacBook Pro is here]

Consider a student or a young professional, or perhaps an underemployed older one, who needs a new laptop. They decide on a MacBook. Apple Upgrade will appear to be the best deal: In its announcement, Apple offers the example of a 14-inch MacBook Pro that retails for $1,999 but that can be had for a monthly lease. Perhaps this person goes for the two-year term, which has them paying $54 a month. Best Buy, which currently has the same computer on sale for $1,849, offers an 18-month loan repayment with $103 installments. Apple’s deal appears to be cheaper: The 24-month lease adds up to $1,296; Best Buy’s 18-month loan lands at the store’s full retail price of $1,849.

Why does Best Buy’s laptop seem more expensive? It’s because the plan is actually designed for you to fully pay off the device. At the end of the 24-month MacBook Pro lease, meanwhile, the consumer will still owe $703, meaning that the actual total price of the Apple arrangement is $1,999—higher than Best Buy’s offer. A person can choose to pay off the full amount, return the device outright, or swap for a new one. This is a lot of money, and so the likelihood is that this customer will just go ahead and do the trade-in, restarting a cycle in which they are bleeding their bank accounts to pay for a device they do not own and possibly will never own. And on it goes, in perpetuity.

[Read: Watch Apple trash-compact human culture]

Apple, which reached a market value of more than $5 trillion, its highest ever, the same day it announced Apple Upgrade, has produced a scheme that will eke out more money from its customers than it did before. (Notably, the new plan does not include AppleCare+, as the previous iPhone Upgrade Program did, so consumers will have to add the cost of the warranty or risk having to continue paying the rental fee on a device that they lose or break.) All of this can feel cynical, but of course many technology companies are attempting some version of the same premise: At this point, I pay monthly or annual subscription fees to Apple, Google, Anthropic, Amazon, Sony, and Nintendo for services layered on top of products that once took far less from me. Doing so grants me access to cloud storage and advanced features that would now be difficult to separate myself from. I walked into the trap myself—even saw it coming—but I am nonetheless ensnared.

Is This What Comes After AI Slop?

2026-07-27T18:01:00-04:00

Most self-published authors can only dream of writing something as popular as Daggermouth. Set in a surveillance state ruled by a masked elite, the novel traces the unlikely romance between the president’s son and an assassin hired to kill him. Since H. M. Wolfe uploaded Daggermouth to Amazon as a Kindle ebook, it has been a viral hit. “Did I finish Daggermouth, or did Daggermouth finish me?” a TikTok book influencer gushed. In February, Simon & Schuster paid seven figures to secure the publishing rights to the book and its sequel, which is due out next year.

Daggermouth has spent months on USA Today’s best-seller list and is ranked No. 1 in Amazon’s “science-fiction romance” genre. It also tops a less prestigious list, one compiled by academics studying AI writing. Among thousands of self-published Amazon ebooks that they identified as containing “substantial AI text,” none was more popular than Daggermouth.

Until recently, AI fiction has been easy to dismiss as slop. On Amazon, new-book releases nearly tripled from 2022 to 2025. (That’s probably not because humans suddenly dedicated themselves en masse to the written word.) Many are blatant copycats of existing titles, seemingly intended to trick readers into buying them. Their sales figures are generally meager; the reviews, indignant. A suspicious number of them feature protagonists with the same names. Last year, readers of one fantasy novel noticed a line that seemed to be responding to a prompt asking a chatbot to write in the style of another author. But if a book as beloved as Daggermouth were written with a chatbot, it would raise an awkward question: Could AI fiction be getting … good?

In a new study, researchers randomly selected more than 14,000 Kindle ebooks and ran them through Pangram, an AI-detection tool. One in five came back as more than 25 percent AI-written; Daggermouth returned 60 percent, according to an underlying data set that the authors shared with me. Those numbers, they acknowledge, tell us only so much. The researchers grouped together text that Pangram had flagged as either “AI-generated” or “moderately AI-assisted” (the latter of which can mean that AI was used to edit or polish human writing, or vice versa). And although Pangram has a low rate of false positives, it is not perfect.

[Read: America has a Pangram problem]

So the study’s authors, led by Tuhin Chakrabarty, a professor of computer science at Stony Brook University, devised another check. They scanned each book’s text for “rare expressions”—strings of at least five words in a specific order—that also appear in other suspected AI books but almost never in human-written ones. Daggermouth had many of them. For instance, the following sentence appears near the end of a multipage sex scene in Chapter 27: “They collapsed together, a tangle of sweat-slicked limbs and racing hearts.” That same sentence, word for word, also shows up in another self-published ebook by a different author. Or consider this line from Chapter 38 of Daggermouth: “Her pulse thundered in her ears, drowning out the murmurs of the crowd.” It’s also in an ebook by another author who has published more than 400 different titles on Amazon in the past three years.

The study has yet to be peer-reviewed, and to be clear, there is no definitive proof that Wolfe used AI. “The suggestion that I used generative AI to write Daggermouth is wholly untrue,” Wolfe said in a statement sent by her lawyer. “I wrote this book myself. I have been outspoken about my opposition to generative AI and what it’s doing to writers, artists, and the creative community. I don’t believe it belongs in the writing process.” Referencing Pangram’s limitations, Wolfe added that “accusations like these have real consequences for authors, and they should not be made on the basis of technology that is known to be unreliable.” Susannah Lawrence, a spokesperson for Daggermouth’s publisher, Simon & Schuster, told me in an email that the company stands behind the book, noting that it went through “the same editorial and production process as our other published titles.” Neither Wolfe nor Lawrence addressed my questions about the “rare expressions” that Daggermouth shares with other recent ebooks.

Chakrabarty, who researches AI writing, told me that Daggermouth has “too many telltale signs” of AI for him to buy that explanation. He cited Chapter 18, “Thank You,” as an example: His research team broke it into 12 chunks for Pangram to evaluate, and the tool classified all parts as AI-generated. (In my own spot checks, the tool classified the first few paragraphs as human-written and the rest of the chapter as 100 percent AI.) I ran this by Mohit Iyyer, a professor of computer science at the University of Maryland who wasn’t involved with the study. An expert on AI-language detection, Iyyer told me that even accounting for Pangram’s fallibility, it’s “almost statistically impossible” for an entirely human-written book to register as 60 percent AI. He added that the study’s broader methodology, which connects AI writing to ebook sales, is sound.

Whether or not you believe that Wolfe deserves the benefit of the doubt, the same questions about how Daggermouth was written apply to a growing number of other hits on Amazon. Chakrabarty’s study found that books written with “substantial” AI assistance made up 20 percent of Amazon’s ebook catalog, 12 percent of sales, and 10 percent of best sellers in their genre.

Yet Daggermouth stands out for more than just its popularity. The second-best-selling ebook in the data set is Inked Athena, by Nicole Fox, an author who has 418 titles listed on Amazon. Daggermouth, in contrast, appears to be one of just two novels to Wolfe’s name in 2025, and its sequel is not due out until 2027—an indication that Wolfe’s process likely involves significant amounts of genuine human craft. Iyyer said that using AI in a way that results in a novel that’s actually enjoyable to read takes “a huge amount of effort by a human author.”

[Read: The future of writing is a lot like hip-hop]

Another difference: Jennifer Prokop, who reviews books for Kirkus and co-hosts Fated Mates, a popular podcast about romance novels, told me that people in her world keep talking about Daggermouth. “Nicole Fox, I’ve never heard a single soul talk about her,” she said. (Fox did not respond to a request for comment.) Prokop sees mechanical prose cropping up mostly in self-published ebooks, for which the dominant platform is Amazon’s Kindle Unlimited subscription service. Just because an ebook is charting on Kindle doesn’t mean people are devouring it. Grifters can now use AI to crank out titles by the dozen and then use all kinds of tricks to entice readers, such as buying hundreds of bogus five-star reviews and paying click farms to juice their metrics. “If you can get 10,000 people to read 10 pages of your book, and you have 300 books you’re putting out,” she said, you can make real money. “But you’re not actually writing romances people want to read.” (Amazon did not respond to a request for comment.)

If Daggermouth is AI, then maybe it’s just an outlier—a book that caught the perfect wave of social-media hype. (A series of controversies surrounding the book, including accusations of the author’s AI use and a backlash to the cover art for a special hardcover version, seemed to galvanize its fans and bring it even more attention.) The notion that AI writing requires a perfect confluence of events to top the charts is a comforting thought. But it might not be true. “Everyone thinks they can tell AI writing,” Christine Larson, a journalism professor at the University of Colorado at Boulder who studies the effect of technology on creative work, told me. “But I don’t think we can tell so much.” Privately, several authors and editors have told her that they’re using the technology to accelerate or improve their output. “I totally buy into the idea that a human working with AI can write something that is compelling,” she said. “I have absolutely no doubt that we will see more of this in the future.”

If she’s right, then the question is not about whether we’ll see AI best sellers. It’s about whether anyone involved with them will admit it. Coral Hart, a South African author who uses AI to help her publish hundreds of books a year under different pen names, told me that she works hard to keep her pseudonyms secret after she received death threats and thousands of one-star reviews following a New York Times story about her. Hart claimed that she has had successful authors join the classes that she teaches on AI writing, though she declined to name them. “Until they stop the bullying, the witch hunts, and the ousting, I don’t think authors are going to be transparent, and I don’t blame them,” Hart said. “I’m certainly not going to.”

The taboo against the use of AI in literature is understandable: Among other things, it’s wrong to take credit for a book that you didn’t write yourself. Shunning authors suspected of employing AI might work if there were any way to tell for sure—which there isn’t. That shaming ritual puts the moral burden on individual authors instead of on the mammoth corporations responsible for the system in which they’re trying to eke out a livelihood. It also tends to collapse any distinction between pure AI slop and human writing that is assisted by AI. The upshot is a state of affairs that feels untenable: AI writing can be lucrative, but only if it’s kept secret. Eventually, something’s got to give.

Even Trump Can’t Spin This One

2026-07-25T07:00:00-04:00

If President Trump has one true skill, it is conjuring up alternate realities and getting his supporters to accept them. The examples are everywhere: dismissing stories he doesn’t like as “fake news” from corrupt media, casting the January 6 rioters as victims, election denial. After he baselessly insisted that there had been rampant mail-in voter fraud in the 2020 presidential election, 11 states made mail-voting access more restrictive. His repeated false claims in 2024 that Haitians in Springfield, Ohio, were eating pets led to real threats against the community. Trump builds a world, and by virtue of his power and influence, all of us end up living in it in some way or another.

That era may be coming to a close, in large part because of the way he and his administration have bungled the war in Iran. Trump seems unable to spin this conflict, divert sufficient attention away from it with a new controversy, or end it outright. His social-media posts, which once acted as a de facto assignment editor for the mainstream media, no longer command the same attention or respect. Now, interspersed with AI slop and inexplicable middle-of-the-night reposting binges, Trump’s Truth Social screeds sound more like the fevered ramblings of a radicalized Facebook uncle. For the past five months, he’s been trying to convince the American public (or at least his Truth Social followers) that the war is over, or ending, or a rousing success. His administration is even retroactively changing on the Pentagon’s website the number of American service members who have died in the conflict. But no one seems to be falling for it. His posts don’t have the juice. His alternate universe is no match for the harsh reality of war.

[Read: The war is lost. Now fix what it broke.]

In the Trumpian vision of the world, the U.S. invasion of Iran was not only necessary but also urgent: In February, Trump alleged that Iran was attempting to rebuild its nuclear-weapons program and had continued developing long-range missiles that were threatening U.S. allies and could reach the United States “soon.” But Trump’s claim is dubious, at least according to a March 2025 U.S. intelligence assessment, which noted that “Iran is not building a nuclear weapon.” Experts also cast doubt on the strength of Iran’s missiles: “There is little evidence that Iran could build missiles that reach the United States in the near future,” Emma Sandifer of the Center for Arms Control and Non-Proliferation told FactCheck.org in March.

Justifications aside, the attacks, which were not authorized by Congress, were immediately unpopular with Trump’s base, which elected him in part for his isolationist tendencies. (On Election Night 2024, Trump even remarked, “I’m not going to start a war. I’m going to stop wars.”) Iran’s predictable retaliation, closing the Strait of Hormuz and drastically cutting off crucial supply routes for oil and other natural resources, subsequently drove up prices for goods around the world—immediately exacerbating the unpopularity of the conflict and shocking the markets. The fallout appeared to take Trump by surprise, and in the early weeks of the war, he used his bully pulpit for damage control: On March 9, with Brent-crude futures spiking to nearly $120 a barrel, he gave an interview to CBS News suggesting that the “war is very complete, pretty much.” Oil futures swiftly plunged to less than $90 a barrel.

Trump used this tactic repeatedly. On March 20, he posted on Truth Social that the U.S. was “close to meeting our objectives” in Iran. Oil futures dropped. On March 23, he signaled that discussions between the U.S. and Iran were productive, leading Brent crude futures to plummet by 13 percent. Each time, Trump offered no real proof of his claims, yet markets responded to his version of the story. An NBC News analysis found 32 examples of the president suggesting that the war was coming to a close. “We don’t need people that join Wars after we’ve already won!” Trump posted on March 7, in response to the United Kingdom considering sending military support. In late March, Trump posted that operations were “winding down”; that same day, he rejected a suggestion from Pope Leo that there should be a cease-fire. In early April, he posted that Iran had asked for a cease-fire; later, Iran called his claims “false and baseless.”

Tenuous agreements between the U.S. and Iran have been broken repeatedly. On July 12, the U.S. resumed heavy air strikes on Iran following an Iranian attack on a container ship in the region. As my colleagues Nancy A. Youssef and Jonathan Lemire recently reported, “The Trump administration is back at war. It just doesn’t want to admit it.” The commander in chief has become the boy who cried “Mission accomplished.”

[Nancy A. Youssef and Jonathan Lemire: War by any other name]

As a result, Trump’s posts have become more erratic. On June 23, in response to rising gas prices, Trump posted on Truth Social, in all caps, to nobody in particular, “EVERYONE, KEEP OIL PRICES DOWN. I’M WATCHING! YOU’RE PLAYING RIGHT INTO THE HANDS OF THE ENEMY. DON’T DO IT!” And his posts no longer seem as able to influence the market. A recent report from Axios claimed that bond-market traders have stopped paying strict attention to Trump’s social-media feeds to read the economic tea leaves: “Researchers from JPMorgan show that for the most part, his social media posts are noise, with very little ability to move the market.”

And yet, Trump is attempting to double down. Last week, Truth Social announced that it plans to sell early access to posts from certain accounts (presumably including the president’s) and is considering charging as much as $100,000 a month. The clear implication is that Trump or his close associates will share government news that will move markets, giving paying subscribers a distinct first-mover advantage. Trump currently owns 41 percent of the stock through a family-run trust in Trump Media and Technology Group, the owner of Truth Social. Thus, the decision to charge access is akin to the president’s family profiting off his position as leader of the free world. (Trump Media and Technology Group did not respond to a request for comment.)

Trump is president, so obviously, any statement he makes attracts attention. But simply being president has never been Trump’s superpower. His chief skill, first as a mogul with an uneven history and then as a politician, is molding his image in a way that reflects how he sees himself: as the genius, adored, yet constantly victimized center of the universe. This, of course, is delusional. Yet somehow, he’s managed to make that fantasy at least partially real: He has, after all, been the main, divisive figure in American life for more than a decade.

With Trump, it can feel as if there are no limits—to his shamelessness, his lies, his graft, or his ability to sow chaos, lose interest, and move on to the next outrage. But it’s possible that we’re finally glimpsing the limitations of his ability to control the narrative. For too long, Trump has been the force that made reality feel flimsy, pliable. But in the Iran conflict, he’s met an immovable object. Now reality may just be catching up.

Why Would Meta Download So Much Porn?

2026-07-24T15:15:05-04:00

Editor’s note: This work is part of AI Watchdog, The Atlantic’s ongoing investigation into the generative-AI industry.

Amid tech companies’ ongoing efforts to vacuum up as much data as possible to train AI models, Meta has taken public posts from its own platforms, scraped massive amounts of content from the rest of the internet, and pirated millions of books to train its AI models. Now the company is being accused of downloading much more illicit material: huge volumes of porn, nonconsensual celebrity nudes, blueprints for 3-D-printable handguns, and millions of passwords acquired by hackers.

Strike 3, the parent company of Vixen Media Group, a prolific producer of adult films, is suing Meta for allegedly downloading 2,973 of its copyrighted videos. But in its efforts to collect evidence about these purported downloads, Strike 3 also captured information relating to many other files that Meta may have acquired over the course of two years: They include images from “Celebgate,” a 2014 hack that resulted in the leak of private photos belonging to Jennifer Lawrence, Kirsten Dunst, and others, plus several collections of deepfake celebrity porn featuring the faces of Natalie Portman, Scarlett Johansson, Elizabeth Olsen, Gal Gadot, and others.

Strike 3’s file-transfer lists include ordinary movies and TV shows, alongside dozens of videos from GirlsDoPorn, which was shut down after six people associated with the site were charged with sex trafficking. Meta allegedly downloaded several individual episodes from the website and two “GirlsDoPorn MegaPack” archives. Strike 3 also presents evidence that Meta made some content publicly available in addition to downloading it.

When I reached out to Meta, a spokesperson told me via email that “these claims are bogus.” Meta also noted in a court filing that Strike 3, which regularly files lawsuits against people who download its work illegally, “has been labeled by some as a ‘copyright troll.’”

This is a complicated case. Piracy can be hard to track with precision, and both parties are clearly conflicted: Strike 3, in seeking damages, wants Meta to look bad, and Meta would prefer not to be associated with this kind of content. Yet if Strike 3’s data are accurate, they would show that participating in mass piracy is a by-product of modern tech development—whether or not any of the material collected is explicitly used to engineer AI models or other programs.

[Read: The hypocrisy at the heart of the AI industry]

Although Meta acknowledged the possibility that employees, contractors, or even visitors to Meta’s offices may have used the company’s networks to download the porn in question, its defense in court was that the material was accessed for “personal consumption” rather than as part of a company project. But neither the sheer volume nor the pattern of downloads looks like personal consumption. The file-transfer logs produced by Strike 3 show sequences of files that are unrelated except for a certain keyword or concept. For example, two files that were transferred consecutively on June 2, 2023—“Bajillion Dollar Properties S01E07” and “First Time Home Buyer Anal Fantasy”—are both loosely themed around real estate, but the similarities likely end there. Judge Eumi Lee, who denied Meta’s motion to dismiss the case, cited other such juxtapositions, such as consecutive downloads of “Teenage Mutant Ninja Turtles (1987-1996)” and a video labeled “Teen Sex Sessions 2 (2012).”

Strike 3 has argued that the content could be used for AI training. A Meta spokesperson told me, “We don’t want this type of content, and we take deliberate steps to avoid training on this kind of material.” In its legal defense, Meta pointed out that its terms of service prohibit users from using its AI products to generate images containing pornography. Even if Meta isn’t building a porn-generating bot, however, tech companies can use “unsafe” content to test guardrails for their systems—effectively telling the software what not to generate. (Lee noted that Meta’s terms of service are irrelevant to the question of whether it wants to acquire porn.)

It’s also possible that Meta is compiling an archive of material for no precise purpose, just as Anthropic acquired millions of books that it says it had no intention of using for AI training but wanted to keep for its “research library.” As AI-training techniques advance, there is no telling what kind of content a company might find useful in the future.

Nineteen of the allegedly downloaded files contain models of functional handguns that can be produced with 3-D printers. A file labeled “5.7 million passwords list” purportedly contains 5,718,107 passwords from accounts that were hacked from 2015 to 2019. The downloads also include password-cracking tools that can be used by hackers to break into people’s accounts. Other files include the movies BlacKkKlansman, The Banshees of Inisherin, and everything made by Studio Ghibli from 1979 to 2020; music by Dua Lipa, Elton John, and Paul McCartney; radio shows and podcasts such as The Howard Stern Show and 1619; and software including Microsoft Windows, Adobe Photoshop, Ableton Live, and NBA 2K23.

The files were downloaded through BitTorrent, a system that allows people to share files among themselves. When you download files via BitTorrent, you typically share those files with other people at the same time; if nobody is sharing the files, then nobody can download them. According to Strike 3’s data, which lawyers from the company told me it regularly gathers in an effort to track the illegal distribution of its intellectual property, devices on Meta’s networks also made some of Strike 3’s videos available to others—a practice known as “seeding”—for as long as three months after fully downloading them.

Meta additionally used BitTorrent to acquire large quantities of copyrighted books around the same time, as shown in testimony from another lawsuit. Some Meta employees were uncomfortable with this activity. According to court documents, Jelmer van der Linde, a former Meta employee who was asked to download books to train the company’s AI models in 2024, told his supervisor in a work chat message that it seemed like a “very very dark grey area. Legally, ethically …” He added that torrenting “copyrighted material is not very legal either.” He was transferred to a different project, and another developer downloaded the books instead. He has since left Meta and now works at the Ellison Institute of Technology in Oxford.

[Read: The unbelievable scale of AI’s pirated-books problem]

To verify Strike 3’s data, I reached out to Tom Chothia, a professor of cybersecurity at the University of Birmingham who has studied BitTorrent. He reviewed a technical description of Strike 3’s data-collection methods and told me that, assuming the records weren’t tampered with, “they have established that people at Meta were uploading” Strike 3’s videos.

One thing that complicates the case is that most of the torrenting did not occur over Meta’s corporate networks. Strike 3’s data show sequences of related file transfers across several networks, including Meta’s. Strike 3 argues that the other networks were used remotely in a coordinated way, perhaps to make the activity harder to trace. Meta has called the patterns a coincidence. But Lee found them compelling, writing that Meta’s coincidence theory “strains belief” and that the activity is likely indicative of “algorithmically coordinated behavior.”

Nearly 300 of the transfers were traced to a private residence in Mountain View, California. Strike 3 claims that this was the home of a Meta contractor’s father, and that the file transfers stopped when the contractor stopped working for Meta. In response, Meta argued that these downloads were “plainly indicative of personal consumption.” According to Strike 3’s logs, the files downloaded to the residence include, in addition to porn, Russian-language books, Chinese movies, and a wide variety of cracked software, among other things.

Unfortunately, in the AI era, illegal mass-downloading by tech companies is not uncommon. Other lawsuits have revealed that OpenAI and Anthropic have also used BitTorrent to download copyrighted books. Whether they have uploaded those books as well, or what other content they may have downloaded through BitTorrent, is not yet clear.

BitTorrent is a strange world in which to find respectable megacorporations. It is dominated by media piracy, and very little legitimate file-sharing occurs. But if AI models are intended to replace human creators, then they have to be trained on as much human-created content as possible. BitTorrent is perhaps the fastest and cheapest way to download high-quality work in large quantities. AI companies have claimed that their models can create original work, but so far, the development of artificial intelligence has involved a lot of theft of other people’s intelligence.

Remember Tesla?

2026-07-24T11:58:00-04:00

Nothing captures the decline of Tesla better than the Cybertruck. When the stainless-steel pickup hit the road in 2023, Tesla was the world’s biggest electric-car company. At the time, Elon Musk predicted that he’d eventually sell 250,000 Cybertrucks a year. In the first half of 2026, according to one estimate, Tesla sold roughly 7,000.

Tesla has faced several self-inflicted wounds. Musk’s affiliation with Donald Trump and his tenure at DOGE was a turnoff for electric-car buyers who lean to the left—which is most of them. A promotional event with the president himself on the White House lawn didn’t seem to do much good. Tesla’s worldwide sales fell 9 percent in 2025. The problem is not just a matter of Musk’s politics: He has gotten pretty bored with selling cars—or at least ones with steering wheels. Besides the ill-fated Cybertruck, Tesla hasn’t released a brand-new passenger model since 2020 as Musk has shifted the company’s focus to robotaxis and robots.

[Read: My day inside America’s most hated car]

It can be easy to write off Tesla, or even forget about it entirely. All eyes are now on Musk’s other trillion-dollar venture, SpaceX, with its grandiose goals of launching data centers into orbit and colonizing Mars. But Tesla has become more important—not less—to the future of electric vehicles in the United States. Of the roughly 463,000 EVs that Americans bought in the first six months of this year, more than half were Teslas, according to figures from Cox Automotive—a jump of nearly 8 percentage points from this time last year. The Tesla Model Y alone claimed more than a third of EV sales.

In some ways, Tesla’s comeback has more to do with the failures of other car companies than its own success. Last year, Congress nixed the EV tax credits meant to encourage Americans to go electric. The Trump administration followed suit by rolling back regulations on tailpipe emissions. The entire automotive industry has been scrambling as a result. EV sales have cratered by about 24 percent so far this year, though they are showing some signs of recovery.

Tesla isn’t immune to any of this. In the U.S., its sales slumped by an estimated 11 percent through June of this year. But the broader downturn makes Tesla that much more crucial to keeping the electric dream alive. In recent months, numerous automakers have yanked EVs from the market, delayed new arrivals, and canceled their plans to debut new EVs. Last year, Ford axed the F-150 Lightning, a battery-powered pickup that was supposed to anchor Detroit’s electric future. (Ford is hyping up a cheaper electric pickup truck—but it’s still in development and won’t come out until next year.) Just last week, Honda pulled the plug on its last remaining fully electric option, the Prologue SUV, after killing off a slew of upcoming models.

[Read: America’s car industry can’t go on like this]

Then there’s the matter of price. The cheapest Tesla will run you a little under $39,000, which is hardly a bargain, but is thousands below the going price for a new car these days. It’s all the more remarkable considering that Tesla makes money from the cars it sells—a feat that has eluded much of the industry. Automakers continue to grapple with the high cost of batteries and of designing new kinds of cars. Consider that from 2022 through 2025, Ford lost $16 billion selling EVs. The company expects its electric-car business to simply break even in 2029. That’s partly why so many automakers sprinted for the haven of high-margin gas-powered vehicles once the going got tough.

As sales of new EVs flounder, used ones are picking up the slack: Americans gobbled up an estimated 128,000 of them last quarter, smashing records. Since Tesla has sold EVs in large numbers for so long—its first mainstream hit was the Model 3 sedan in 2017—it carries the market. One of the cheapest paths to an EV that can run 250 to 300 miles or more in a single charge is a used Tesla.

Musk’s car company is crucial to the future of EVs in another way. It dominates the infrastructure of the electric era: plugs. One of the biggest speed bumps to wider electric-car adoption is the challenge of where to recharge. Tesla has been blanketing the country with its Superchargers since its early days in 2012, and now operates about half of all fast-charging plugs in the United States. This is great marketing for Tesla, of course. But it’s also a win for almost anybody looking to give up gas. In recent years, Tesla has opened up most of its formerly exclusive stations to other EVs.

No matter what Musk says about Tesla’s AI future, he may have no choice but to pay attention to his car business. This week, Tesla reported that despite surging revenue, its operating profits in the second quarter of 2026 were the lowest in six years, in part because of massive expenditures on AI. Musk is also hemorrhaging cash at SpaceX. All of that money has to come from somewhere. Beyond America’s troubled EV industry, Tesla is doing more than just surviving. A spectacular second quarter of deliveries worldwide stunned Wall Street analysts and points to the beginnings of a bigger rebound. (Musk did not respond to my request for comment.)

The EV market would be in far worse shape right now without Tesla. But the real shame is that Tesla could be doing so much more to get drivers to go electric. The automaker has managed to hang on in the U.S. by serving up little more than the microwaved leftovers of its limited, aging lineup. While Tesla’s automotive innovation has stalled, Chinese companies are releasing one affordable, high-tech model after another. Perhaps Tesla could roll out a truly budget EV. You know, something like the $25,000 car Musk once touted onstage but reportedly shelved in favor of the Cybercab, a robotaxi without a steering wheel. A less polarizing, smaller pickup could probably do a whole lot better than the Cybertruck has. At this point, almost anything would clear the bar.