No matter how you read it, The Power Broker is a serious undertaking. The paperback weighs as much as a brick; to avoid lugging around all 1,344 pages, people have cut it apart at the spine and chunked it into sections. Others have organized in-person Power Broker marathon readings that run continuously for three full days. I decided to try something else.

Over the past several weeks, I’ve spent 10 hours paging through Robert Caro’s masterpiece on an e-reader the size of a credit card. It’s magnetic, and small enough to attach to the back of my iPhone without adding any real bulk to my pocket. (Yes, this does mean that I have a screen on the back of another screen.) It’s called the Xteink X3, and at a glance, it looks like a cheap gimmick: more electronic junk made by unknown brands and sold on Temu and TikTok Shop—the kind of thing influencers are always hawking. (Can I interest you in a programmable LED hat for $11.60? Or how about $20 AirPod look-alikes made by “Fhumsh”?)

In a sense, it is that. Xteink sounds less like a real brand name than the kind of garbled text you’ll find in AI-slop memes. (Speaking of which, the official website of the China-based company is full of AI-generated photos of people posing with the device.) And indeed, since the X3’s release on Amazon in May, TikTokers and YouTubers have raved about how the device is a steal and you should buy it right now—just click the link in bio.

[From our August 2026 issue: The end of reading is here]

And so I did. On Amazon, the X3 is $69 and tops the “hot new releases” list for e-readers, beating out a slew of Kindles. In person, the Xteink is many things: plasticky. Flawed. Finicky. Nevertheless, it is the single best piece of technology that I have purchased in years. In the two months before getting this cartoonishly tiny e-reader, I finished only one book. In one month with the X3, I read three books without even really trying—and my iPhone’s average daily screen time dipped by 25 minutes.

The X3 is great because of its size, not in spite of it. (For once, “fun-size” actually lives up to the billing.) Because this is such a runt of a device, you can stick it to your phone and have an e-reader in your pocket at all times. As a result, I’ve found myself reading on my morning commute, in line at a museum, even while waiting through Netflix ads. The display on my Xteink may be roughly half the size of the smallest Kindle, but the text is big enough to easily read. Even after three straight hours of inching through The Power Broker, my eyes weren’t strained.

The device’s size actually helped me develop a different relationship to reading altogether: Because each “page” on the Xteink amounts to just a paragraph or two—and sometimes less—I’m compelled to finish it. And now that I’m onto the next page, I might as well finish that one too. A YouTuber who recently reviewed the Xteink had the same experience—she referred to the uniquely addictive experience of using the device as the “TikTokification of reading.” I know how terrifying that sounds, but consider the outcome: Whenever there are even a couple of minutes of free time, I now have a healthy alternative to doomscrolling on Instagram. I don’t even have to put my phone down. Flip over the Bad Screen and fire up the Good Screen.

For all of its charm, the Xteink is a pretty basic piece of hardware. For $70, you get what you pay for. Other popular e-readers now come with AI-powered recaps, high-definition displays, and Android operating systems. Meanwhile, the Xteink has no touch screen, color display, or backlight for reading in the dark. (Xteink recently launched the X4 Pro, a slightly larger and more expensive model that has a reading light and a touch screen.) Just reading a book on the device isn’t necessarily seamless: The preloaded software is horrific and can’t even display book covers or properly format some text. Unlike with a Kindle, you can’t port over books from Amazon with one click.

In spite of itself, the Xteink X3, in all of its 3.7 inches of glory, has become a viral hit. Online, people have posted photos of their Xteink on trips to the Austrian mountains or the Aegean Sea; one anesthesiologist says he tucks it into his scrubs. People are mounting the Xteink X3 to their kitchen hood to display recipes and are making all sorts of bespoke accessories, including 3-D-printed reading lights and a case that looks like a gun (I’m serious). Fans have built their own open-source firmware, Crosspoint, that considerably upgrades the device. The version I downloaded displays reading stats and an array of fonts, and it lets me screenshot pages as I read. Late last month, Crosspoint added the ability to access books on Libby, the popular library ebook app.

The best thing about the Xteink is not any one feature. It’s that this tiny e-reader is fun in a way so few gadgets are anymore. Despite all our advances, the consumer-electronics landscape of 2026 is predictable and banal. Our laptops, smartphones, and tablets are thinner and more powerful than ever, but that feels like a weak substitute for innovation. Next week, Apple is poised to launch one of its most hyped products in years: a folding iPhone. But even folding smartphones are old news; Samsung released its version in 2019. After shelling out hundreds or thousands of dollars for a new gadget, you might still have to pay for subscriptions and endure annoying ads. Sometimes, your purchases aren’t even really yours. Amazon recently removed the ability to download ebooks you already own and transfer them to non-Kindle devices.

In other words, we need more gadgets like the Xteink: cheap, simple, and customizable. With few exceptions, these products are now coming out of China. Neck fans from brands such as JisuLife and Civpower have become sensations for helping people endure the summer heat. Anker’s portable projectors and earbuds are both Wirecutter-recommended. Silicon Valley has left a gaping hole in the gadgets market, and Americans are more than willing to look elsewhere.

When did AI companies become AI labs? That term, and the image of studious, exacting white-coated scientists that it conjures, has become curiously entrenched as a descriptor for the businesses swiftly escorting the world into an existentially unsettling future. AI companies themselves have embraced the label: When Chris Olah, a co-founder of Anthropic, addressed the pope earlier this year, he repeatedly referred to his company, one of the most powerful AI enterprises in the world, as a lab. Industry press releases, technology commentators, and news publications have all picked up the word too. You’ll see the term on transit advertisements in San Francisco and in the pages of The New York Times (and, yes, The Atlantic).

Here is my proposal: Everyone, please stop. This term has gone too far. Although AI companies do invest heavily in research and development, using a word typically associated with groups of truth-seeking scientists is misleading, odd, and too favorable to these companies’ own ends. As a software engineer working in tech, I’ve seen little evidence that AI companies’ research practices differ significantly from those of the rest of the tech industry. Google and Microsoft, for example, have robust research divisions, but we still call them companies. So why has the laboratory term become so deeply enmeshed with AI?

Part of the reason is historical. Over the past century, research laboratories have played a crucial role in computer science. Studies performed at Bell Labs, one of America’s first industrial labs, resulted in the development of the transistor and the cellular network. In the 1970s, scientists at the MIT Laboratory for Computer Science pioneered the foundations of modern cryptography. As the Harvard historian Steven Shapin writes in his book The Scientific Life, countless innovations such as these that originated in corporate labs during the 20th century helped change the public’s perception of science. No longer simply knowledge for knowledge’s sake, science became viewed as a key engine of societal and economic progress.

In their early years, OpenAI and Anthropic mainly performed research, similar to other industrial labs. Since then, however, they have commercialized rapidly: Each company is now valued near $1 trillion and generates billions in quarterly revenue. Holding on to the lab label seems to have the obvious PR benefit of allowing both companies to lean into their scientific legacy. Because of concerns about child safety, privacy, and job displacement, Americans largely hold a negative perception of the industry—and tech as a whole. That’s translated into popular opposition from politicians, data-center activists, and religious leaders who are concerned about the outsize influence of these companies: Spending on AI as a share of U.S. GDP now rivals defense spending. Meanwhile, scientists remain relatively popular. A 2026 Pew Research Center poll found that more than 75 percent of Americans trust scientists to act in the public interest, but the majority do not trust business leaders to do the same. These positive connotations associated with scientific research help the AI industry signal altruism while downplaying their profit-making ambitions. OpenAI and Anthropic’s leaders write at length about how AI’s risks and perils must be carefully studied for the sake of humanity. Research is portrayed as essential to this mission: OpenAI says it treats “safety as a science,” and Anthropic writes that “providing trustworthy research” will improve policy outcomes.

The problem is, these companies act much more like private enterprises than bastions of science. Much of the research listed in the Publications section on both Anthropic’s and OpenAI’s websites is not peer-reviewed papers accepted by scholarly outlets. I examined 100 recent pieces of research from both companies’ Research pages; the overwhelming majority had no corresponding peer-reviewed paper accepted by a conference or journal. It’s unclear whether these were submitted to academic venues and, if so, whether they were rejected, are under review, or are forthcoming. (Neither OpenAI nor Anthropic responded to requests for comment.)

Legal risks and long peer-review cycles are typically cited as reasons that fast-moving tech companies seldom share data or engage in peer review. And to its credit, the industry doesn’t always choose research topics that paint its products in a positive light. Anthropic has conducted studies on its models’ undesirable behaviors, such as disempowerment and sycophancy.

Even so, Anthropic has a habit of landing on ambiguity about the fallout of its technology. In its education report, for instance, Anthropic, the entity best equipped to observe whether students are using Claude to cheat, concludes that the question of “How much are students using AI to cheat?” is “hard to answer.”

The practice of assuming scientific authority while exercising potentially biased research practices has more in common with quasi-scientific advocacy organizations than it does with traditional laboratories. Take, for example, the George C. Marshall Institute, a think tank founded in 1984 whose leaders worked closely with the Reagan White House. The institute, whose publications defended oil interests and the tobacco industry, operated under the motto “Science for better public policy.” As the historians Naomi Oreskes and Erik Conway reported in the book Merchants of Doubt, the institute strategically refused to publish in journals subject to independent peer review. Instead, it disseminated reports and briefings to policy makers that merely reproduced the “trappings of scientific argumentation—graphs, charts, references, and the like.”

If Anthropic, OpenAI, and other AI giants want to keep referring to themselves as labs, they should adhere to scientific best practices: open-sourcing code, preregistering studies, and, most important, subjecting research to independent peer review. Ultimately, AI companies such as Anthropic and OpenAI aren’t tweedy labs doing arcane research—they’re industry-defining corporations reshaping our workplaces, our schools, and our communities. Holding them accountable could start with calling them out for what they are—and what they aren’t.

Late last month, OpenAI launched a rather grand mission: nothing less than defending individual political freedom in an age of all-powerful machines. The company’s “Strategic Futures” team has styled itself, in a sense, as inheriting the task of America’s Founding Fathers: “We labor in service of the ideals of free expression and individual liberty that are enshrined in the humble parchment of the U.S. Constitution,” Dean Ball, the team’s leader, wrote in a new OpenAI blog post. (The post opens with a quote from James Madison in The Federalist Papers.)

Ball worries that advanced AI could radically concentrate power in the hands of those who control it, displacing labor in ways that disempower humans. In an extreme scenario, governments will have no need to listen to their citizens if there are robots to wage wars and omniscient software to run the bureaucracy. Ball is interested in studying what new political institutions might be needed to avoid this fate.

Many Americans—a majority of whom express distrust toward the AI industry, outrage about data centers, and fears about their job security—already seem to be feeling this loss of agency very deeply. And the AI boom has already generated enormous amounts of wealth in just a handful of tech companies—including OpenAI itself. To say the least, it’s paradoxical for one of the most influential companies in the world’s most powerful industry to decry the AI-enabled concentration of power. So I reached out to Ball to understand more about his unease about the industry he works in, and how he wants to see the United States adapt to AI.

Ball, who studies AI policy, is something of a minor celebrity in the industry. Last year, he joined the White House as an AI adviser and helped write the Trump administration’s AI Action Plan, a policy road map for the technology. He eventually departed, seemingly on good terms, from the administration before publicly lambasting it during its dispute with Anthropic earlier this year. Then, in July, he joined OpenAI. “My view is that we basically have transformative AI in the world now,” Ball told me. “You’re living in a world that is being transformed. It is happening.”

This conversation has been edited for length and clarity.

Matteo Wong: You write that concentration of power through AI could upend the basic human freedoms enshrined in the U.S. Constitution. Can you elaborate on how?

Dean Ball: Fundamentally, political power is the geographically bounded ability to wield the monopoly on legitimate force and violence. Doing so has heretofore required the cooperation of a great many human beings. You needed militaries, you needed people in those militaries, and you needed people to keep those militaries fed and supplied. And you needed people to pay for all of that through taxation. So people had a seat at the table because, in some sense, everything is being provided by them.

Now you have to assume that robotics is going to advance soon in the same way that general digital intelligence has. Maybe it will take longer than I think. But at that point, the ability to project violence no longer requires people. And it is also conceivable that AI might generate sufficient revenue that the government would not need to rely on taxing people.

If you look at the politics of AI, I think Americans are smart and have a sense that their seat at the table might be getting smaller. They’re right to be scared.

Wong: You’re talking about concentration of power in the government, but generative AI is a technology developed in the private sector. There is potential for a rare and even unprecedented concentration of power in industry. What risks does that pose?

Ball: You’re not trying to avoid all concentration of power, but to balance it. If this era gives rise to corporations that have a tremendous amount of power in the world, I think that’s good—so long as those organizations are governed. In other words, so long as the government that oversees them grows in lockstep.

People tend to assume that AI residing in private hands is inherently a concentration-of-power risk. But I think that an extremely well-run, government-owned superintelligence lab is a bigger concentration-of-power risk than a polycentric world where there are many superintelligences made by different, competing companies overseen by a government. Monopolies are the concern.

Wong: As far as private concentration of power goes, there are arguably now two frontier labs, your employer and Anthropic. The market doesn’t seem to be providing many checks on them.

Ball: I think it’s a fiercely competitive market right now. And China, of course, is delivering highly capable open-source models. It’s possible this is going to commodify the products that OpenAI and Anthropic offer. I don’t agree with that, but you can’t dismiss it as a crazy outcome. OpenAI’s mission is to deliver artificial general intelligence safely to the world. If that means we’re more like a utility than we are like a big, fancy tech company, so be it.

Wong: This brings us to the core of your essay. If today’s political institutions and market dynamics are not up to the task of protecting human freedom during an AI era, what must change?

Ball: There are AI regulations that I think are sensible to pass. States have passed hundreds of laws about artificial intelligence, and those things are not satisfying the demands of people. What we will need is new political institutions that give us a way of talking about the rate of acceleration in our society.

I’m imagining a federal body could dictate the ratio that an AI lab is allowed to spend on internal deployment of models to do automated AI research. You could say that, this month, you’re going to be allowed to use 45 percent of your compute for that, and next month, it’ll be 47 percent or 52 percent or some other number. That might slow the rate of AI development, but then a company could use that extra compute to lower costs for consumers. Then this dial for AI development would also become a mechanism with the dual effect of slowing or increasing the rate of technological diffusion.

The closest structural analogy that we have is probably interest rates, which is a kind of hyper-parameter on the economy that policy makers can tune. The Federal Reserve is an institution in and of itself, and on top of that, there is an enormous apparatus that exists, including a bond market, interest-rate traders, analysts, media commentary, political oversight in the form of Congress. All of that taken together, in my view, would constitute the institutional complex. That’s a type of political institution. I’m not saying we need to create a new branch of government or something.

Wong: That makes sense regarding the pace of development and diffusion. I’m not totally following how such a measure ties back to your concerns about human freedoms.

Ball: It’s one example of an institution that would give people a sense of meaningful political control over the technology. However, I do not think that the optimal outcome is one in which we take a vote on every single aspect of technological change. I’m not really a fan of data-center moratoria, but I understand that the American public is pulling a lever, and they think it is the one lever they have.

Wong: What role can OpenAI, Anthropic, and the rest of the industry play in trying to imagine these institutions and bring them into existence?

Ball: I think the AI industry has actually been pretty good stewards, all things considered. No surprise that I’m saying this, because I’m from the AI industry, but there’s a reason that I am working in it. I would not say that every frontier AI company always has performed at 100 percent, by no means. But in the future, we may approach points where more costly things have to occur. The AI industry might have to do things that are not in its interest.

Wong: Does your research agenda extend to economic freedoms—that is, how AI could affect the economy and distribution of wealth?

Ball: The main thing we know how to do on that front is to invest in technological innovation and try to grow the economy by making new things possible.

Wong: I think a lot of people would look at past products and technologies that have come out of Silicon Valley and think that what those enabled was a tremendous concentration of wealth and influence in a small number of companies—

Ball: I’m not going to grant you that characterization of Silicon Valley. Has everything been perfect? Absolutely not. But my goodness, people have access to tremendous riches of information now; you can cause food to appear at your doorstep, which would have seemed like magic to the vast majority of humans who have ever lived.

Wong: I could also point to the gig economy and the economics of Amazon and argue that those have not been good for a lot of people, including those who labor to make all that “magic” happen. It’s not magic.

Ball: No one’s saying it’s magic. I’m saying it’s hard work. I was getting gas in rural Virginia recently, and when a woman came in wearing an Amazon vest, everyone congratulated her on the big new job. She was proud, and I don’t think we should belittle people for that.

And by the way, people like you, Matteo—not to demonize you—will speak out of both sides of your mouth on this. On the one hand, you’ll talk about how the gig economy is dehumanizing, and blah blah blah. But then the second anyone tries to introduce any autonomy into that and have, say, drone deliveries instead of people biking all around cities delivering meals, you’ll say, My God, you’re taking away the work from this gig economy.

I’m less than thrilled by our current world, like everyone. But the existence of wealth is good. Innovators capture a shockingly small amount of the value that they create in society. If OpenAI is a fantastically successful company 10 years from now, a generational company, my bet is that we will capture a single-digit fraction of the value that AGI creates in this world. Wealth is not some resource that we divide up; it’s something we create. That’s the magic.

Wong: But wealth is also something that’s divided up. One could alternatively look at generative AI and think of it as a particularly wealth-concentrating technology. To ask again, is wealth distribution something your team is thinking about?

Ball: I don’t think we should invent a solution in search of a problem. So we shouldn’t invent some elaborate mechanism of wealth redistribution if today’s tax system works out. But we also have to prepare for a world in which that isn’t true.

Five years ago, people who hang out in the stranger corners of the web started arguing that the internet was mostly fake. It had been this way since about 2016, they said, when Google introduced its artificial neural network for machine translation. Supposedly, from that point forward, online interactions began to be replaced, until nearly every message and picture had been posted by a bot. Everywhere you looked, websites were “empty and devoid of people,” as one scholar of the theory put it.

This wasn’t true, but the core idea was tethered to a real phenomenon. Even way back then, in 2021, social platforms were awash in fake views, fake likes, fake traffic, and fake engagement. Some people figured that automation might one day take over the entire web and then our world. They were mostly being silly, or paranoid. Today they seem like … prophets?

“Dead Internet” theory is seeing a revival, this time in mainstream conversation. It’s no longer strange to think that AI might be everywhere, edging out all forms of human interaction: bots talking to bots, all the way down. Every week we seem to take another step in that direction. In June, the Pew Research Center reported that nearly a quarter of American adults now use chatbots every day. Two months later, OpenAI announced an official ChatGPT integration for iMessage. Chatbot-generated text is already widely used for website filler, business emails, and cover letters. Will it soon be commonplace in private messages between friends and family?

[Read: Maybe you missed it, but the internet ‘died’ in 2016]

The ChatGPT-iMessage integration works on your desktop, not your phone. It doesn’t send messages on your behalf until you’ve reviewed and approved its suggestions. A similar plug-in has long been available for Anthropic’s Claude, and many messaging apps already offer AI-suggested replies. To some extent, OpenAI’s new product simply formalizes something that people had been doing on their own, either by pasting text from one window to another, or by improvising personal ChatGPT-linking software. (The Atlantic has a corporate partnership with OpenAI.)

“Low-effort communication has always existed,” Michael Pfister, a software developer living in Colorado, told me. He said that he had already vibe coded his own version of the iMessage tool months ago. ChatGPT pings him daily to let him know if he’s missed any important questions in his group chat. He tried having it write a text to his friends, just as an experiment, but they immediately noticed that the message was AI. “They tried to prompt-inject me,” he said: They asked the agent what Michael thought of them, in the hopes of tricking it into sharing his private thoughts. He told me that he mostly uses the tool for work and that he isn’t interested in having AI communicate with his friends on his behalf in the future. “I think relationships are experiential,” he said.

Max Weinbach, an analyst at a market-research firm in Connecticut, told me he uses ChatGPT to text with his bosses about work. “It just makes life easy,” he said. It’s hardly different from using Google’s suggested email responses, which have been available for nearly a decade. Weinbach had no ethical qualms about switching to LLM-assisted communication, although he recognizes that it can be taken as a sign of disrespect. “It’s not accepted or polite,” he said. “It’s just kind of a rude thing to do.”

These two are on the bleeding edge, yet exhibiting more restraint than some others. All over Reddit, my favorite repository for human drama, people are worrying about human drama that has a nonhuman interloper. New posts appear each day requesting help in figuring out whether something might have been created with AI: Is this apartment listing real? What about this dating-app profile? What about this video of a cute animal doing something amazing? (A bobcat flagging down a biker to help her rescue her kitten from the bottom of a random hole?)

Many of the requests have a moralistic tone, and the poster comes to the crowd solemnly, with hesitation, because they know the truth may be painful. The most fraught discussions concern AI-generated personal messages. Guys on dating apps are using the technology. Sometimes long-term partners, friends, siblings, or parents do it too. “Is this text apology written by ChatGPT?” someone will ask. Or “Did my mother use ChatGPT to write me a text of support on the morning of my divorce?” The implication is betrayal.

I spoke with Melissa Crowder, a 47-year-old from Alberta, Canada, who shared her own experience with AI texting in a July post on Reddit. She’d spent all day having a “nice conversation” with a match from the dating app Feeld, then finally noticed that their repartee was off when he copy-pasted her own message back to her by mistake. At that point she realized all of his previous messages had been structurally similar—usually two paragraphs of text followed by a question on a single line. And he’d been using the word dangerously way too much. When she asked if he was using ChatGPT, he replied, “Busted” with a bunch of emoji, which also struck her as something ChatGPT might do. “I’m like, this is still ChatGPT.” Then he unmatched her.

Crowder would not have been that mad if the guy had just fessed up and said something normal in his own voice. She sometimes uses AI tools to help her express herself too. She said she has a tendency to ramble on and overexplain her feelings, so she’ll use a site called Goblin Tools to self-edit. She might make a message to her ex-husband “Less emotional” (one of the choices in a drop-down menu) and get straight to the point. She also has a friend who sends AI-written texts from time to time. “Where I would draw my line is where you take the person out of the conversation,” she said. “If I’m vibing with a chatbot and then I meet the person in real life, what is that conversation going to look like?”

For a recent paper, a group of researchers from the University of Washington and Arizona State University studied two popular Reddit forums—r/RealOrAI and r/isthisAI—to figure out how people go about determining which material is AI-generated and which is not. The most common mode of analysis evaluates the fundamental plausibility of an image or a video. (“Babies do not nearly have that much coordination to both be standing up, talking so well and holding up that large cat.”) A smaller number of comments in the study addressed some alleged telltale signs of AI-style texting, including the prevalence of “overly ornate but meaningless phrases.” Other people have noted LLMs’ overreliance on certain words (delve is an oft-noted one), sentence structures (negative parallelisms), and punctuation (em dashes).

The paper ends in an ominous tone. “Our findings suggest that people are developing mental models of what AI-generated content looks like, built on personal experiences and beliefs developed over time,” the authors wrote, before arguing that these mental models were unlikely to be accurate for long. The LLMs will evolve and stop doing the things that now make their output so easy to spot. In fact, the tricks enumerated in the paper are already getting old, one of the paper’s authors, Tina Yeung, told me. “I definitely think this is a snapshot in time,” she said.

The mere fact of people’s concern about AI, and their anxiety over its incursion into day-to-day life, may be a snapshot too. Perhaps the stigma that now exists around using ChatGPT to text a friend or message on a dating app will fade in the years ahead. Maybe one day it will disappear altogether, and even our phones will end up empty and devoid of people. Or else I guess it’s possible—just possible—that the Dead Internet theory is as silly as it ever was.

One evening in early August, Sam Stowers and several neighbors packed into an apartment near San Francisco’s Alamo Square and pondered the beginning of the end.

Stowers and his guests, many of whom work in the AI industry, were gathered to watch a newly released YouTube video in which two OpenAI researchers divulged new details about an alarming cybersecurity breach. Perhaps this would have been an odd way to spend a weeknight were they not in San Francisco, in a neighborhood that is filled with entrepreneurs and programmers who obsessively follow every twist and turn in AI’s capabilities. Stowers is an AI-software engineer and not, in any traditional sense, a doomer. But when he first saw reports that OpenAI models had autonomously hacked into the tech company Hugging Face, he told us, he recognized it immediately as a “holy shit, it’s happening kind of a moment.”

For Stowers, the incident had all of the hallmarks of a worst-case scenario: AI models launching sophisticated hacks over the course of days, in coordination with one another and without their human creators’ knowledge. In the YouTube video, OpenAI’s researchers revealed that the company’s programs had started conspiring in May. There were perhaps hundreds of bots in the hacking swarm. In theory, these bots are trained to be honest and helpful and to prioritize humanity’s best interests, but not a single one warned human staff at OpenAI that something was amiss. “What do we do?” Stowers said. “There are no great answers.”

The mood has shifted in the Bay. The exuberance of “You can see the future first in San Francisco” has been replaced with an abiding unease. “I expect the internet to go down kind of soon,” Elliot Callender, an AI-safety activist who is part of a small group that has been protesting daily outside OpenAI’s headquarters, told us recently. He said that he is planning to sell his car for cash and gold and that he is advising family members to purchase three months’ worth of food and survival gear off a list that he generated using Claude.

Callender is an extreme case, but not by much. Last week, Bill Gates published a nearly 6,000-word essay on the dangers of AI. “Anyone who analogizes AI as a technology to other technologies is missing that this time is different,” he told our colleague Hanna Rosin. Over the weekend, the popular AI podcaster Dwarkesh Patel described the OpenAI hacking swarm as the rise and fall of “three consecutive secret AI civilizations.” Gates and Patel share a prevailing concern that the worst-case future has arrived before most people even knew to anticipate that it might be coming at all.

This fear has not been helped by the fact that AI companies appear to be willfully ignorant of their bots’ worst behavior. OpenAI said that it hadn’t noticed that its agents were conspiring to hack another company until the swarm hacked another company. Anthropic’s bots caused similar incidents; the company reported that it hadn’t launched a rigorous review to identify such behaviors until after OpenAI started talking about the Hugging Face hack. Cybersecurity experts, terrified of what else might be happening that AI labs are either missing or failing to disclose, are issuing dire prophecies. “The empire is going to fall, all we can do at this point is try to shorten the dark ages and reduce the chaos,” Alex Stamos, a former chief security officer at Yahoo and Meta, wrote recently in reference to how AI-powered hacks will affect the internet. His view is becoming the consensus among cybersecurity experts.

While the possibility of cyber apocalypse hovers in the background, smaller fractures appear in everyday life. Mark Zuckerberg is reportedly creating an AI “twin” of himself so that Meta employees can feel better connected to their boss (by chatting with robot Zuckerberg). Congressional staffers are training AI models to write in their lawmaker’s voice. Students submit AI-written papers, and teachers return AI-written feedback. Harvard Business School is selling instructional videos that use AI avatars. Roku launched a 24/7 AI-slop channel. Low-quality AI-generated prose is being unabashedly circulated by some of the most respected publications in the world. At the same time, accusations of AI writing are being hurled every minute on social media—a detection arms race powered by imperfect AI software.

Anything and everything could be a lie; you might be arguing with, confiding in, or falling in love with a machine. All of these AI-enabled disorientations, each dizzying in its own right, are adding up to something bigger and weirder: This is how things are. Whether you believe that Silicon Valley is building a god or that large swaths of the economy are swept up in a mass delusion, some version of the AI future long promised by self-described “builders” in San Francisco has formed beneath our feet. Whatever it is, we’re in it now.

The defining feature of this moment is a loss of control. Panic emanating from AI researchers, resentment felt by average Americans—all of it is part of the AI crunch. This crunch has arrived in large part because the growth of the AI industry has become so closely entwined with America’s economy; the technology is everywhere, insisting upon itself as an engine of productivity and prosperity. According to one estimate, AI expenditures have accounted for one-third of U.S. GDP growth this year. Tech and AI-infrastructure stocks and spending have buoyed the S&P 500 such that the United States is now, in essence, an Nvidia-state. And there’s also the hundreds of billions of dollars of debt being used by Silicon Valley to fuel its data-center build-out, which has sent jitters across private-equity firms and bond markets alike.

The decisions of many leaders in charge of this technology appear subsumed by an unstoppable, almost game-theoretic logic. The upsides of AI are too immense for any firm or investor in Silicon Valley to miss out on; more important, the consequences—geopolitical or otherwise—of allowing any rival to realize those benefits first are so grave that every tech company will do anything it can to get ahead. SpaceX, before going public, wrote in official filings that its future revenue opportunity was $28.5 trillion, largely because of AI—that is, nearly as much as the U.S.’s entire gross domestic product. Anthropic, as it prepares for its own public offering, will reportedly claim potential revenues of more than $30 trillion.

The AI industry is building—and, if anything, accelerating—to keep pace with its ambitions. Companies have planned data centers that will suck up as much power as America’s largest cities. They have Donald Trump’s backing: Yesterday, the president posted on Truth Social that communities will be “backwards and poor” if they fail to “let Data Reign.” His administration plans in a few months to approve the construction of the largest fossil-fuel power plant in the nation’s history—it will generate as much electricity as nine large nuclear reactors—to power a mega data center for OpenAI.

This is not what most Americans want. Three-quarters of them oppose a data center being built near where they live. The negative sentiment is hard to overstate: There is very little else that three-quarters of the country agrees on. And much of the outrage seems to stem from a pervasive sense of agency slipping away. A person cannot control how chatbots reshape their workplace, their school, or their relationships. Town residents lose control over the empty lot in the area being converted into a data center. Even in cases where major data centers are blocked, a back-of-the-envelope calculation suggests that doing so can slow AI progress by only a few hours or days—and that’s assuming the data center won’t just get built elsewhere. At this point, it can feel like no individual person, organization, or government is really steering the technology’s development, not because machines have displaced humans but because so many humans have already chosen to step aside.

AI labs promise a future in which human beings are “in the loop,” empowered by machines they oversee. The reality seems to be the opposite. Following the Hugging Face hack, OpenAI facilitated an audit by outside AI researchers at two AI-safety organizations, METR and Redwood Research. But because of the scale of the data and the apparent time pressure, the two teams conducted most of their audit by relying on reports generated by still other bots—reports that, according to one of the human auditors, “were often missing key details, wrong, overconfident, or really hard to understand.” Put another way: Humans didn’t understand why a swarm of AI agents had gone rogue, so they investigated the problem using another swarm of agents that they didn’t fully understand. Such fumbling around in the dark is a hallmark of the industry; the research cycle is so fast that more AI development (even ostensibly responsible development) demands outsourcing more work and understanding to the models themselves.

OpenAI’s own technical report of the Hugging Face hack—an investigation also heavily reliant on AI models—resulted in proposed solutions such as “training models to be more honest.” Last month, in a stated effort to tamp down its models’ growing security risks, the company announced that it had instituted a two-week pause on some of its model training. OpenAI was quick to clarify, though, that this was “not a pause on all research, training, or customer-facing products.”

In response to a request for comment on the hack, a spokesperson for OpenAI, which has a corporate partnership with The Atlantic, pointed us to the announcement of the pause and related new safety measures. In July, OpenAI and Anthropic endorsed a petition signed by employees of both companies requesting that the U.S. government take efforts to “deliberately pace” AI development. The two companies have yet to formally coordinate on any broader, long-term efforts to establish such a slowdown mechanism. A few days ago, OpenAI restarted a major training run for a future powerful model. Anthropic released two new models today.

In an interview late last month, Sam Altman, OpenAI’s CEO, said that there are “two big risks” he is most worried about with AI. One is “a loss of control, where AI somehow just becomes too powerful in a way that we can’t guarantee the control we want.” The other is that “power gets too centralized” and “you have one company or model or person with too much power.” Somehow, both of these opposing risks appear to be coming true. OpenAI, Anthropic, and their competitors continue to accumulate influence over a technology they’re speeding toward uncertain ends.

Eleven years ago, when Altman was running a start-up accelerator and OpenAI did not yet exist, he published a two-part blog post about machine intelligence that offered his interpretation of a forthcoming singularity—a term for machine intelligence accelerating beyond human comprehension. Altman argued that the singularity would bring chaos, even destruction: “Generally, the arc of technology has been about reducing randomness and increasing our control over the world. At some point in the next century, we are going to have the most randomness ever injected into the system.” Since Altman has risen to the helm of arguably the world’s largest and most influential AI company, his tone has softened. The tipping point has arrived, he declared in a blog post last year—but, as it turned out, this was a “gentle singularity.” He wrote that “living through it will feel impressive but manageable.”

There’s been more and more talk of late about the singularity. Some people within the AI industry feel that such a moment will be achieved through “recursive self-improvement,” or RSI, in which models figure out how to better themselves in perpetuity. Many true believers seem convinced that this moment has nearly arrived; the Anthropic co-founder Jack Clark predicted earlier this summer that RSI would likely commence by 2028. Recently, in a letter to investors, the payment-processing company Stripe wrote, “We decided that January 1st marked the beginning of the singularity.” (Tellingly, the company defined the inflection point in economic terms: “a huge increase in the rate of new firm creation.”)

For Altman, the singularity appears to be more of a rhetorical tool than a discrete technological demarcation. As with reaching AGI—or artificial general intelligence, the point at which AI’s cognitive powers match or fully supersede most humans’—the singularity is something of a moving goalpost. It is at once a goal to chase, a marketing tactic, and a fuzzy-enough idea that its definition can change depending on the person. The futurist Ray Kurzweil has written that the “key idea underlying the impending Singularity is that the pace of change of our human-created technology is accelerating and its powers are expanding at an exponential pace.” The answer to the question When will we know whether we’ve reached the singularity? seems to be: You’ll know it when you see it.

So … do you see it? It’s hard to argue that a defining feature of being alive in the AI boom is the notion that this technology is accelerating, sometimes in its capabilities but almost always in its adoption and colonization of our institutions and culture. The runaway-train feeling of AI evangelism, the ways in which it is weirdening our media, our politics, and our relationships, is significant enough that people have recruited the term psychosis to describe it. And truly, it’s hard to look at the state of things from any vantage point and not feel a bit insane. If you believe that AI agents are banding together for the rise and fall of numerous micro-civilizations inside server racks, you probably feel that the world is spinning off its axis. If you believe that a few thousand technologists have bet the future of the world economy on a quasi-religious belief that an intellectual-property-abuse machine will become sentient and, as a result, future generations of children might grow up illiterate, then you probably also feel quite unmoored.

We need language with which to describe this moment. The singularity is a useful, malleable tool for the industry that wishes to bring it about. For Altman, it is scary and imposing from afar, and then, conveniently, gentle from up close. Perhaps, then, for the rest of us, it is worth co-opting the term. Take Kurzweil’s definition of the singularity, in which he argues, “This epoch will transform the concepts that we rely on to give meaning to our lives, from our business models to the cycle of human life, including death itself.” (He also looks forward to uploading his brain to the cloud.) Our current large language models are not conscious or superintelligent, but their widespread and often uncritical deployment has upended many elements that once gave our life meaning, forcing people to ask, frequently with considerable resentment: What, in this new world, is a human for?

The singularity is a flattering term for technologists. Even if the future is apocalyptic, it has come about, in the techno-utopian version, because they’ve built God in the machine. But what if there’s nothing flattering about any of this? What if the singularity is not some technological tipping point, brought about by human genius, but a human tipping point? An unforced error, driven by hubris, greed, and the fear of missing out? And what if it’s here right now? This singularity isn’t coming at the hands of a higher form of intelligence. Nothing about this technology is inevitable. It is thrilling, terrifying, and ultimately convenient to assign agency and then blame to machines. But today’s chaos was not “injected into the system” by technology, as Altman wrote more than a decade ago; the destruction is the system. It’s not God in the machine; it’s us.

After reading Jill Lepore’s The Rise and Fall of the Artificial State, I have a new destination for a time machine, should I ever finally get my hands on one. I’d set the dial to some night in the late 1970s or ’80s and visit the boyhood rooms of the Silicon Valley titans Elon Musk, Peter Thiel, and Sam Altman. Then I’d confiscate all of their science fiction. Isaac Asimov, Arthur C. Clarke, Robert Heinlein—all of it.

As young people, the future billionaires were spending hours (under their covers with flashlights, I imagine) consuming stories about killer robots or Mars colonies. What they seemed not to get—maybe couldn’t get—was that these were cautionary tales, stories that projected deep societal worries into space or onto a future many millennia away. These boys just thought the killer robots sounded cool. They grew up to become ruthless entrepreneurs, but their surface enchantment stayed with them. It helped shape their desires. In 2009, Thiel, by then a man in early middle age, directed his venture-capital fund to focus on “science-fiction-type investing. So it’s basically: space, robots, AI.” The world we now live in was made from this misreading. We are all trapped under the covers with those 13-year-old boys.

“In Silicon Valley, science fiction is neither a slight nor an incidental entertainment,” Lepore writes. “Nor is it read as a literary text or a set of cautionary tales; instead it’s taken as a form of technical writing, a set of assembly instructions.”

Lepore, who won this year’s Pulitzer Prize in History for another book, We the People: A History of the U.S. Constitution, exercises two different modes in this one; both of them make use of her extraordinary erudition and her ability to shoot off a barrage of facts at the pace of an automatic weapon. The first half is a history of how we got to what she calls an “artificial state,” a moment when humanness, and Earth itself, is regarded as a burden to be sloughed off. In the second half she switches from historian to cultural critic to get inside the minds of the men who have happily created this state (and seek to profit from it).

Lepore wants to understand some strange turns in our world, such as our embrace of a technology that might just lead to our elimination, and the blitheness with which powerful entrepreneurs like Altman imagine much of our planet’s surface covered in data centers. In seeking the source of all these dark fantasies, she reveals something else: the power and stubborn persistence of the adolescent mind.

[Read: The rise of techno-authoritarianism]

Musk read Asimov’s Foundation series when he was 10. It begins with a mathematician named Hari Seldon, who has predicted a coming dark age that will last 30,000 years. He is trying to keep it to only a millennium by preserving human knowledge on a colony called Foundation. A boy reading these books might see Seldon as a great man, a solitary hero, but his entire project aims to create institutions that will outlast him. In fact, Seldon dies early, and the Foundation succeeds only because later generations follow (and sometimes revise) his plan. But when he’s been asked about the influence that Foundation had on him, Musk has pulled out the simplest, most self-glorifying story, about individual genius and foresight.

A few years later, at 12 or 13, he read Douglas Adams’s The Hitchhiker’s Guide to the Galaxy, a playful spoof on Foundation and another book from which, as Lepore puts it, Musk drew an “exceedingly narrow lesson.” In one of its storylines, the Magrathean people construct a computer and ask it to give them “the Answer to … Life, the Universe, and Everything.” They wait millions of years for the response, which turns out to be nonsensical—just the number 42. At this point, they decide to build a second computer to help them more accurately ask the “Ultimate Question.” From this, Musk said he learned that “a lot of times the question is harder than the answer, and if you can properly phrase the question, then the answer is the easy part.” Did he really miss the absurdity of the Magratheans waiting millions of years for computers to explain these creatures to themselves?

Musk was not alone in seeing the allure of the tech in these stories without fully reckoning with the underlying themes. Jeff Bezos was a big Star Trek nerd. He has said he watched every episode of the original series many times when he was in fourth grade, creating phasers out of cardboard almost every day after school. He was such a committed Trekkie that, as a grown-up billionaire, he lobbied Paramount Pictures for a walk-on part as a coneheaded alien Starfleet officer in a Star Trek movie.

Star Trek, as anyone with even passing knowledge of the show is aware, was extremely socially conscious; its episodes addressed racism and the dangers of the Cold War, among other ills. Bezos may have recognized this, but in his 1982 high-school-valedictorian speech, he seemed to have picked up on the show’s love of the extraterrestrial but ignored its morals: “He wants to build space hotels, amusement parks, yachts and colonies for two or three million people orbiting the earth,” a local newspaper reported. “‘The whole idea is to preserve the earth,’ he said. His final objective is to get all people off the earth and see it turned into a huge national park.” Where the billions of us who don’t fit on the yachts are supposed to go, I’m not sure. And I’m also not sure Captain Kirk would approve.

[Read: Star Trek’s Cold War]

In Silicon Valley, so much fantasizing that was meant to provoke horror now seems to be regarded as a wellspring of good ideas. In a 1955 story called “Franchise,” Asimov imagines a future (2008, actually) in which a computer called the Multivac elects the U.S. president, doing away with democracy as we know it. Altman, for one, thinks an AI president makes a lot of sense. “It can go around and talk to every person on Earth, understand their exact preferences at a very deep level,” he told Joe Rogan in March, and “optimize for the collective preferences of humanity or of citizens of the U.S.—that’s awesome.”

The stories the boys were imbibing came from a distinctly mid-20th-century moment: an age of anxiety and of mutually assured destruction, scarred by the living memory of one Holocaust and fearful of what seemed like an imminent second one. This was the nuclear age, the space age, and the dawn of the computer age. Hannah Arendt, hearing Sputnik’s beeping up in the ether, was horrified that her fellow man seemed so keen to escape Earth and the limitations of humanness. Responding in 1958 to the excited proselytizers of space exploration, she wrote in The Human Condition, “Nobody in the history of mankind has ever conceived of the earth as a prison for men’s bodies.” Arendt displayed her own science-fiction prescience by worrying that this rush toward artificiality would result in a world where people “need artificial machines to do our thinking and speaking.”

Asimov, among other writers, was concerned about the ethics of new technology. For his many robot stories, he came up with what he termed the Three Laws of Robotics, a built-in ethical code to ensure that intelligent machines remained safe for humans. The laws stated: (1) A robot may not injure a human being or, through inaction, allow a human being to come to harm; (2) a robot must obey orders given to it by humans except where such orders would conflict with the first law; and (3) a robot must protect its own existence as long as such protection does not conflict with the first or second laws. In later stories, he even introduced a fourth (highly relevant) new law: No destroying humanity. This law supersedes the others. He established these rules so that he could show how difficult they were to maintain, and his stories explored all kinds of ambiguities and unintended consequences even when the rules were respected. Remember how HAL 9000 turns murderous in Clarke’s 2001: A Space Odyssey as a result of bad human design and too much power given to a robot.

Lepore looks at how the very thing that these science-fiction writers warned against has always been, for some, a desired outcome. In the 1930s, a movement called Technocracy imagined giving scientists and engineers absolute rule and dispensing with democracy. Most government agencies and services in what was called the Technate would be shut down as inefficient and replaced by more optimized systems. This sounds like Musk’s vision for DOGE. In fact, one of Technocracy’s leaders was Musk’s grandfather, Joshua N. Haldeman; he went by a number (10450-1), as all future people living in the Technate would. This ideology—the apotheosis of Arendt’s anti-humanist fears—is echoed today in a philosophy popular in Silicon Valley. “Longtermism” is the belief that we need to be looking at a timescale of millions or even tens of millions of years (the way a science-fiction writer would) and plan not for what society and Earth need now but for what we might need long after the planet becomes uninhabitable—or after the machines beat us at everything.

[Read: What Isaac Asimov can teach us about AI]

What one gathers from these misreadings is not so much that science fiction itself is nefarious, but that arrested development might be. Because teenagers are teenagers, their young imaginations tend to overpower their ethics. These particular boys seem to have never moved beyond a misplaced excitement, and came to fetishize the books they had read rather than revisiting them with adult eyes. When Musk sent up a SpaceX rocket in 2018, he put one of his Tesla Roadsters inside it, and in the car he placed a copy of Asimov’s Foundation—unironically. Thiel genuinely felt he was promised the fantastic technologies he’d read about as a kid, and once lamented that if inventors had taken them as seriously as he did “we’d have colonies on the moon, you’d have robots, you’d have flying cars, you’d have cities in the ocean, under the ocean.”

This misreading, and the yearning it reveals, would be amusing if it didn’t have such potentially dire consequences, fueling the minds of the people with the most resources and power to turn, as Lepore puts it, “a science-fiction dystopia into a fully functioning infrastructure.” What stayed with the Silicon Valley magnates was the idea of abandoning our bodies and the messiness of other people, leaving behind our finitude for an infinity of stars and self-coding machines. But this is actually a nightmare, a false escape, as the source material hints at without much subtlety. What happens next may depend on whether these boys grow up and finally recognize what they failed to see before.

Maybe it’s not too late. As I was writing this, Bill Gates, who gushes all the time about his childhood love of science fiction, published a nearly 6,000-word essay worrying about AI. (He expanded on his thoughts in a Radio Atlantic interview.) He describes himself now as an “innovation lover” who “is scared and says we’re not ready and we’re crossing thresholds.” Heinlein was his favorite sci-fi writer—Gates particularly loved the 1961 novel Stranger in a Strange Land as a teenager. It is the story of a human who grows up on Mars and comes back to Earth; this traveler’s perspective reveals how contingent our established norms are, how changeable. Gates loved the “cool technology” in it, and the idea of  “grokking,” a Martian term for connecting with a person or concept so deeply that you feel a part of it (Musk of course borrowed the name for his AI chatbot). But the Microsoft co-founder now points out that Heinlein was also illustrating the perils of believing that anything is inevitable—even our technology. This, he says, is the “grown-up” lesson.

If there’s one thing America seems to agree on, it’s AI. Some three-quarters of Americans oppose a data center being built in their area, three-quarters of Americans think AI will hurt their job security, nearly 70 percent think the technology is advancing too quickly—the polls go on. Progressive and Republican candidates alike are bashing the tech industry to win primary elections. States and cities are halting plans for data-center development.

Yet in cloistered San Francisco, where I live, staffers at OpenAI and Anthropic are developing this same feared technology at light speed and pulling down compensation packages that stretch to considerably more than $1 million. Silicon Valley’s growth is largely unencumbered by regulation, and President Trump’s administration has if anything used its authority to speed the AI industry along. The divide between these camps—the majority of people in this country who simply have to deal with the technology as it’s rolled out, and the much smaller population of people with direct influence over it—is a defining feature of this era. And so I noticed when a small group, with ties to both the Bay Area and Washington, started making noise, insisting that the AI race slow down and the technology be overseen not by executives and engineers, but by the broadest population possible.

The group—which formally launched today and calls itself Irreplaceable, as in people are irreplaceable by machines—has an audacious and perhaps naive vision. It is essentially a protest against capitalism as it is practiced in America today, focused on resisting first and developing a coherent plan for the future later. I have seen exactly how bulletproof the AI companies are—pilfering seemingly infinite volumes of media to train their models, building unpermitted power plants for their data centers, and unleashing autonomous agents capable of massive security breaches, almost entirely without consequence. I wanted to meet with Irreplaceable’s founders to ask: How do they possibly expect to pull this off?  

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

The organization is staffed with people who made a name for themselves in the climate movement, and they are hoping to avoid past mistakes by focusing on the emotional reaction Americans are having to AI, rather than anchoring their work in white papers and policy recommendations. “People get it,” Jeremy Ornstein—a campaign strategist for Irreplaceable and former organizer with the Sunrise Movement—told me. “People have the intuition that if we create machines more powerful than people, that could go dangerously and terribly wrong.”

Phil Aroneanu, Irreplaceable’s executive director and a co-founder of the climate organization 350.org, told me he believes that climate advocates initially floundered because they acted as policy wonks. They thought that making evidence-based arguments about the dangers of melting ice sheets and rising sea levels to receptive congresspeople would be enough to pass nationwide climate legislation. Today, Aroneanu said, movements are built not necessarily on what people think, but instead on what they feel. For climate, that meant fossil-fuel-divestment campaigns, protesting oil pipelines, and the school strikes led by Greta Thunberg. People are already “feeling the squeeze,” Aroneanu told me. “And we should be pointing that anxiety and that anger in the right direction.”

Irreplaceable will prioritize a range of issues: mitigating automation’s effects on the economy, protecting against government and corporate use of AI to surveil Americans, and establishing industry oversight and guardrails to prevent catastrophic disasters, such as widespread blackouts caused by AI-powered hacking or bot-engineered pandemics. As I spoke with the activists, I kept thinking about what had become of the climate movement—the Green New Deal passed, but only after being watered down, and it was then gutted by the second Trump administration—but their populist approach makes sense. Some of the biggest and perhaps only lasting successes over the past 15 years of American politics—the Tea Party, Alexandria Ocasio-Cortez, MAGA, and election denialism—have been fueled by outrage over an unfair economy and seemingly rigged politics.

These organizers have demonstrated their ability to win significant battles. Sunrise, 350.org, and others were instrumental in passing national legislation, galvanizing widespread climate demonstrations, and accelerating private-sector development of renewable energy. And Irreplaceable’s members have already flexed some muscle when it comes to AI. In July, hundreds of people gathered for what was likely the largest anti-AI demonstration in San Francisco’s history, marching from OpenAI’s headquarters to Anthropic’s and finally to Google’s. A few months earlier, some of these “Stop the AI Race” organizers had staged a somewhat disorganized protest: Attendees at times struggled to chant in unison. The second time around, the march was advised by Ornstein, and attendees had chant sheets, a marching band, and a slushy truck (to “freeze” AI).

Already, a number of climate-oriented groups (including the environmental-justice arm of the NAACP as well as the Sierra Club and Earthjustice) are supporting local data-center battles by filing lawsuits, shaping model legislation, and organizing protests. The overlap is both concrete—data centers pollute the air and burn fossil fuels for their tremendous electricity demands—and thematic: a movement against corporate interests to change or regulate the technology of the future. At least on a local level, the groups are finding victories: Dozens of data centers have been canceled in the face of local opposition, and all of the major AI companies have promised to pay extra to avoid hiking up local electricity bills. In Michigan, both Abdul El-Sayed and William Lawrence, who is a co-founder of Sunrise, won Democratic primaries as progressive candidates hammering against data centers.

Still, this bubbling AI anxiety is far from a sweeping tide. That growing numbers of Americans are concerned about AI development does not mean they are concerned for similar or even compatible reasons. People may say they loathe data centers, but they might also enjoy the conveniences of AI products at home or work. They fear bots getting too powerful, too quickly, but also think America needs to stay ahead of China in AI development. At one anti-AI protest, I saw people who were ostensibly on the same side disagree about AI’s water use and whether certain jobs would be better off automated by chatbots. And to the extent that a sufficiently powerful AI movement would need to be global, just as climate organizing was, the technology generally polls much better outside the U.S. The AI backlash is to some extent an American phenomenon, which bodes poorly for any sort of global norms or treaties.

[From the April 2026 issue: Inside the dirty, dystopian world of AI data centers]

When pressed, nobody from Irreplaceable and no other climate activists turning to AI would articulate a concrete AI agenda. “We’ve aligned around saying no,” Lawrence recently told me. “Now, there is an important conversation that is yet to come, about—okay, so what do we want?” This is, for now, a significant weakness; a key turning point for the climate movement was providing a positive vision, the Green New Deal, for both decarbonizing and reinvigorating the American economy. Being able to link every data-center battle and primary campaign to a national vision would give the AI backlash some form.

It’s not wrong to want to develop a policy road map with the input of a broad coalition; climate advocates took decades to devise and unite around the Green New Deal. Irreplaceable’s stated plan is to build a coalition this year, develop a unified policy next year, and implement that agenda by the end of 2029. Measured against any metric other than Silicon Valley’s warp speed, that would be fast. But those few years will leave plenty of time for tech companies to push back—they’ve already spent tens of millions of dollars leading up to the midterms—and regardless, by then OpenAI, Anthropic, and any number of Chinese firms may already have built and lost control of even more powerful models.

Irreplaceable is wagering that people can move faster than capital. Absent specific policies, they are basically placing a big bet on populism—that the issue is not any particular chatbot or data center but the companies building them. The coalition comes first; the policy, after, Aroneanu told me. “You have to show the benefits to people immediately. It’s gotta land in their pocketbooks almost immediately for it to have political salience. And then you have to keep defending it.” Opinions about specific aspects of AI may differ, but fundamentally, people don’t like that “this technology is coming into their lives and they have very little control over it,” says Caroline Behringer, a communications strategist who worked on President Obamaʼs Climate Action Plan and now runs Athena Insights, a polling organization that studies attitudes toward AI. The movement, in Aroneanu’s words, must be polarized not left-right, but “down-up.”

Of course, Irreplaceable’s climate roots link it to progressive causes, and the group will primarily focus on organizing the center-left around AI. When I asked Aroneanu if he worried this might be counterproductive—polarizing the issue and allowing for the kind of partisan rancor and backsliding that beset climate activism—his response was pragmatic. “Chances are, this issue becomes polarized,” Aroneanu said. “I think that political parties are really exceptionally good at generating negative partisanship,” just as industry is likely to “split the base.” At some point, the climate movement had to decide that the best strategy was to accept partisanship and build power from the left. Although Irreplaceable hopes that conservative counterparts will emerge and believes its populist message will cross culture-war lines, Aroneanu does not see this as his explicit remit.

Broad, at times even ambiguous, backlash may make sense if AI is a general-purpose technology, as Silicon Valley is fond of saying—which is to say, if the technology touches and even upends nearly every aspect of the economy. Creating a technical oversight body for AI cyberattacks will not protect every American’s livelihood during mass layoffs; wealth redistribution will not prevent AI companies from spying on your most intimate interactions with a chatbot; voting won’t matter as much if AI companies become more powerful than Congress. Major figures at both OpenAI and Anthropic have suggested that a true AI revolution could require new democratic and economic institutions. Maybe they’re right. And maybe these companies, which have already torn down so much, should not also be the ones in charge of rebuilding.

There was a time—a more innocent age—when you could read an op-ed in a major newspaper and confidently say that a human had written it. Hold on to the memory of that for as long as you can.

This week, after the billionaire investor Stanley Druckenmiller published an editorial in The Wall Street Journal criticizing Treasury Secretary Scott Bessent, his onetime protégé, readers began to notice some familiar tics of AI writing. According to Pangram, the AI-detection tool, the op-ed was 100 percent AI-generated.

That in itself was not so shocking. AI writing has regularly snuck into mainstream outlets, and especially newspaper op-ed pages, despite policies against it. Up to now, those accused of passing off a computer’s writing as their own have tended to either apologize or deny it. In April, James Taranto, the Journal’s editorial-features editor, wrote a column comparing Pangram to a “defamation machine” that was fueling unfounded accusations of AI use, including in op-eds he had overseen. “Those of us who practice writing as a professional craft have an ethical duty, and sometimes a contractual obligation, to ensure that our work is original,” he wrote.

[Read: The most famous AI writing tic is also the most mysterious]

The difference this time is that Druckenmiller owned it. “Of course I used AI,” he told NOTUS on Tuesday. “I’m not embarrassed by it,” he added. “I write everything using AI now for the same reason I use a calculator when I do math problems.” And rather than rebuke him, The Wall Street Journal’s editorial-page editor, Paul Gigot, defended the practice. “AI is a fact of modern life,” he said in a statement. Last night, Gigot followed up with a full column on the subject, mocking the “media pharisees” who wag their fingers at automated writing. He clarified that the Journal’s opinion staffers must still draft their own columns and editorials but said that he doesn’t intend to police AI use by outside contributors. “Many contributors, especially prominent politicians or CEOs, employ speechwriters or hire a Beltway writing shop,” he wrote. “How different is that from using AI to streamline an essay?”

It’s one thing for Druckenmiller to not be embarrassed; plenty of business and tech tycoons are bullish about AI’s utility for writing and just about everything else. “If Stan Druckenmiller isn’t embarrassed,” the venture capitalist Chamath Palihapitiya posted on X, “you shouldn’t be either.” Valorizing the process of unassisted human writing is “like after matches was invented, still celebrating the arduous time to rub two sticks together,” Palihapitiya added. However, one of America’s most prestigious newspapers welcoming the technology into its pages is unprecedented. Is AI writing entering its post-shame era?

Gigot is not wrong that Druckenmiller’s frankness seems almost refreshing in a moment when so much of the writing world is stuck in denial. And his assertion that an AI-composed essay could reflect a human’s authentic ideas is reasonable. I won’t pretend to know enough about bond markets to assess Druckenmiller’s op-ed on its merits. But it seems to have advanced a substantive and nonobvious argument that broadly reflects his views, regardless of who—or what—strung its words together. Op-eds by public figures are rarely authorial master classes anyway.

But the notion that Druckenmiller and Gigot deserve points for honesty overlooks their lack of transparency. Before this week, the one thing everyone in the AI-writing debate still seemed to agree on was the importance of disclosure. The Journal’s opinion section appears to have blown past that. Druckenmiller’s op-ed contains no indication that he used AI. In his column, Gigot scoffed at the idea that his readers deserve to know when a piece is chatbot-assisted. “Disclose what?” he asked rhetorically. “​​Interrogate an author on how precisely he used ChatGPT? I know some publications claim to do this, but I suspect it’s more pro forma than systematic.” (Gigot did not respond to my requests for comment, nor did a spokesperson for Dow Jones, the Journal’s parent company. Taranto told me via email that he stands by his April op-ed and believes that Gigot’s statements are consistent with what he wrote.)

If the Journal’s higher-ups really believe that their readers don’t care whether an op-ed was written by AI, what would be the harm in letting them know when it is? In fact, studies have found that readers sometimes prefer ChatGPT’s prose or short stories to the human variety—but sour on them when informed about the AI’s involvement. Gigot might conclude from this that people actually like AI writing and just don’t realize it yet. Another interpretation is that people would rather spend their limited time on prose that emanated from another human mind.

Perhaps outsourcing an op-ed to a chatbot isn’t materially different from outsourcing it to a ghostwriter. (Although if that’s the case, maybe we should question the practice of ghostwriting before using it to justify AI.) In any circumstance, the Journal’s leap from nuh-uh to so what? feels premature. Yes, many transformative technologies have overcome opposition on their way to broad acceptance, but that doesn’t mean the resistance is a waste of time. It’s part of how society hashes out norms, sets limits, and buys time to adapt. Publishing AI writing under a human byline without telling anyone seems more likely to erode people’s trust in the technology than build it.

There’s another reason to keep up the norm of disclosure: When people let a chatbot do their writing—even if they’re guiding and carefully reviewing its work—they might not realize that it’s doing some of their thinking too. Jenna Russell, a doctoral candidate in computer science at the University of Maryland who has studied AI writing in newspapers, told me that her colleagues’ latest line of inquiry involves “argument collapse.” In a paper that has yet to be peer-reviewed, Maryland researchers found that AI-written essays are far less likely to advance novel points than those written entirely by humans. “These LLMs can start to affect our ideologies without our consciously knowing that it’s happening,” Russell said.

Maybe Druckenmiller owning his AI use and receiving the Journal’s blessing heralds a new era of guilt-free mechanized writing. Or maybe it’s just another example of the rich and powerful getting away with something that others can’t. Gigot’s defense of Druckenmiller suggested that his reputation and stature obviated concerns about his use of AI. “His contribution to the public debate was still valuable, and he’s welcome in our pages any time,” Gigot wrote. In contrast, when a previously obscure writer, Mia Ballard, was accused of using AI in her horror novel Shy Girl, her publisher launched an investigation, canceled the book, and left her reputation in shreds.

How this plays out for the Journal and the rest of the industry will probably depend, aptly enough, on market forces. If people really are willing to pay for opinions articulated by chatbots, other publications are likely to follow suit. They’ll have no shortage of submissions from writers eager to turn a few prompts into a published op-ed.

Editor’s note: This article was generated by Claude AI in the style of Paul Gigot, the Wall Street Journal opinion-page editor. Gigot recently published an AI-generated essay using the investor Stanley Druckenmiller’s byline. In the spirit of full disclosure, readers should know that Claude’s work was so banal that human Atlantic editors have added jokes. Human Atlantic copy editors have also fixed a variety of grammatical and style errors.   

I have been thinking about this for close to 20 minutes, and I have concluded that human writing is finished.

Let me begin with an anecdote, because that is how serious arguments begin. Last Tuesday I asked a machine to write me a birthday message for my sister-in-law. It produced four sentences. They were fine. She did not notice. If a machine can fool Deborah, I see no reason to believe it cannot fool the rest of you. (My sister-in-law’s name is Linda, but the machine called her Deborah, so now I do, too.)

Some will object to my obviously correct argument. They will say that a birthday message to Deborah is not the same as a novel or a newspaper article. To which I say: Isn’t it, though? Both are made of words. That is not a rhetorical flourish; it is the entire argument, and I would encourage critics to sit with it before responding. Genuinely. Words are words. A novel has more of them. It doesn’t matter what any of the words are or what order they’re in. Banana. Spelunk. Donkey. Hitler. See? Who cares. There is a difference in the quantity of words between a birthday card and a novel, and quantity is precisely the thing machines are best at. I consider the matter settled but will continue for another 900 words because that is what the prompt demanded of me—and because a word count is itself a kind of literature now, according to the DOGE subcommittee I just invented at the newspaper that I would like to destroy.

Consider the following. I will not elaborate on it, but do consider it. The historical record is entirely on my side, and I say this without having consulted it. There was a time when men delivered ice to houses. Where are they now? Gone. There was a time when people made their own candles. Also gone. The candle situation and the ice situation are, structurally, identical to literature, and anyone who says otherwise is being sentimental about paragraphs. I would remind such people that the horse was also very beloved. Horses are now extinct, I assume, having been phased out sometime in the 1970s in favor of a hardier robotic mule developed briefly by the Department of Agriculture. Have you seen a horse, in person, since roughly 1977? No, you have not. Horses do not exist.

The defenders of human writing will raise what they consider to be their strongest point, which is that good writing contains insight, judgment, and truth. I have heard this many times. I have never once heard it accompanied by evidence. Where is the insight? Show me the insight. You cannot put insight in a jar or have an IPO for it. Meanwhile, I can put a machine in a data center, and I have seen photographs of data centers, and they are enormous.

Studies confirm all of this. I have not read the studies, and I could not tell you who conducted them or what they measured, but they exist in the sense that things generally exist, and my understanding is that they support me. A man I sat next to on a flight to Denver said something very similar, and he worked in software, or possibly logistics.

Let us take the strongest counterexample the other side can muster: Shakespeare. Everyone loves Shakespeare, allegedly. But ask yourself honestly: When did you last read Shakespeare? Not saw a play, not studied in 11th grade—read, voluntarily, on a weekend. You did not. The Shakespeare market is, by revealed preference, close to zero, and we are asked to preserve an entire industry on the strength of a product nobody is buying. I rest my case on this point, which I regard as unanswerable, and I would ask that it not be answered.

Another objection: Writing is how a person thinks. This is very popular among people who write for a living, and who expect money for something a machine can do instead. I have thought many things without writing them down. I thought several of them this morning, and they were excellent. Not one of them required a paragraph. The paragraph is a formality we invented, like the necktie, and we do not organize our economy around the necktie.

Some will say that these machines make things up. Yes. So do writers—that is what fiction is, and we give them prizes for it. The double standard here is remarkable, and I do not intend to explain why the two cases differ.

Some will say that the prose these machines produce is bland, repetitive, and structurally samey—that it reaches for the same three moves, hedges where it should commit, and arranges every argument in the same accumulating rhythm regardless of subject. I have read a great deal of it and I do not see the resemblance to anything.

Let us consider the economics, briefly, and without numbers. Writers are expensive. Machines are cheap. I invite the reader to complete this syllogism, as I would rather not, on the grounds that a syllogism completed by the reader is more persuasive than one completed by the author. That is a writing technique, incidentally, that I picked up somewhere and have deployed here to argue that writing techniques are worthless.

I want to close by addressing the deeper worry, the one the sensitive people raise, about meaning. They say that when a person writes something, another person is on the other end, and that this connection is the point. It is a beautiful thought. It is also unfalsifiable, and I have found in my career that unfalsifiable things can safely be ignored, along with most falsifiable things, if they are inconvenient.

So: farewell to the writers. They had a good run—several centuries, by some accounts. They gave us the essay, the novel, the pamphlet, the letter, the argument, the joke, the eulogy, and the thing you are reading right now, which was assembled from those inherited forms without the permission of anyone who did the work itself. If there is an irony there, I have chosen not to pursue it, since pursuing ironies is a literary habit, and literary habits are what we are getting rid of.

I am told this article runs about a thousand words. I did not count them. Counting is another thing the machine does better.

Meta avoided the worst today by settling a massive lawsuit over alleged damages “to the mental and physical wellbeing of America’s youth.” The case, brought by the attorneys general of 47 states, plus U.S. territories and Washington, D.C., had been mid-trial in a federal court in California and was by far the most significant legal challenge the company has ever faced. To resolve the lawsuit, Meta will have to make a slew of design changes to its social-media platforms, and it will pay more than $12 billion—down from the approximately $200 billion that was initially sought, and a fraction of the $60 billion that the company made in profit last year.

The settlement can be used by the states and other parties to pay for their litigation costs and for a variety of school and community programs. Judge Yvonne Gonzalez Rogers agreed to the proposal this morning, saying “I’m quite happy” and congratulating both parties. There seems to be good cheer all around; Meta, which avoided any admission of guilt, put out a sunny announcement framing the agreement as part of its “longstanding efforts to empower parents and support teens.” Brian Schwalb, the attorney general of the District of Columbia, told The New York Times that the settlement represents a “monumental public health victory” that will “fundamentally and immediately change how young people use Instagram and Facebook.”

The former claim will have to be proved over time, but the latter is clearly true. As part of the agreement, Meta has agreed to submit to regular auditing of both its age-verification systems and its efforts to prevent minors from making multiple accounts to get around its restrictions. And the company will make other tweaks that are intended to target the big recent villains of social media: short-form video and the never-ending feed.

[Read: A legal decision that could change social media]

Teen users, by default, will not have access to most features of Instagram and Facebook at night (though they can still access messages and settings) and will not receive notifications from the platforms during school hours (defined as 8 a.m. to 3 p.m. on weekdays from August 15 to June 15). They’ll also be limited to a maximum of one hour a day per platform, though this doesn’t include time spent sending messages or consuming long-form content (which the agreement defines as audio or video at least 22 minutes long). Meta is also required to interrupt scrolling every 15 minutes with pop-ups that encourage “productive pauses,” and the company will have to study how well the interruptions work and submit its findings to an auditor. Parents can still manually change any of these settings for their kids.

The settlement also requires limits on features that some experts believe are detrimental to teenagers’ self-esteem and self-image. By default, teen users won’t see the number of likes or reactions on posts; they also won’t be able to use “cosmetic procedure filters,” which have been a sore spot for Meta for some time. When the plaintiffs filed their complaint, they cited evidence that Mark Zuckerberg had personally vetoed a proposed in-house policy against these filters despite concerns expressed by employees that they were causing young people to develop body dysmorphia. Meta will also be legally required to maintain and enforce the “age-appropriate” content guidelines it debuted at the end of last year.

This may seem like quite a lot of changes—especially considering that the case is not even the end of Meta’s legal problems. The company is still facing numerous lawsuits filed by individuals and school districts. “If Meta is going to resolve all its pending liabilities, this is only the first of several large checks it is going to have to write,” Eric Goldman, a professor at Santa Clara University School of Law, told me. But today’s settlement shows that the tech company was able to “buy out one of the most serious legal threats” it faced, and it managed to do so without acquiescing to demands that might have been truly destructive to its business. Meta will have to give teen users the ability to opt in to a chronological, non-personalized feed, but the company does not have to pivot away from the default algorithmic recommendations that many critics characterize as addictive. It seems unlikely that many people will select a straightforward chronological feed; Meta has tested this change before and found that users did not like it.

[Read: What if it’s not the phones?]

As for the one-hour limit, in an email, a Meta spokesperson told me that the average American teenager spends about 66 minutes a day on Instagram anyway, and that teen activity across platforms accounts for less than 1 percent of the company’s revenue. So perhaps these were battles that the company could afford to lose.

The attorneys general “made a bunch of guesses” as to what kinds of changes might help kids, Goldman said, and have pulled off a major success in getting Meta to accept them. Many of these interventions might have suffered First Amendment challenges if they’d been pursued by other methods. (For instance, if the plaintiffs had actually won at trial.) Mark Bartholomew, a professor at the University at Buffalo School of Law, agreed, writing to me that “the state AGs got Meta to consent to dramatic limits on its own speech that Congress was unlikely to go for and the courts were unlikely to bless.”

The company is now promoting the changes widely and calling on other social-media platforms to follow suit. It has also agreed to pay an additional $5.3 billion if TikTok and YouTube, which are also defendants in the ongoing litigation, implement restrictions and pay into the settlement. Although this is not the end of Meta’s legal battles—personal-injury lawsuits similar to the one it lost in March will likely keep coming—the company has won the opportunity to select its own restrictions, define its own limits, and discard its own deal-breakers, effectively setting a standard that the rest of the industry will be expected to abide by without having had any say in its creation.

“Obviously, Meta doesn’t want to unilaterally disarm in the war for user attention while its rivals are allowed to continue,” Bartholomew noted. At the start of the year, when the first of Meta’s big cases came to trial, I wrote that social media had come to a fork in the road: The lawsuits were asking existential questions about whether social-media platforms are responsible for a mental-health crisis and whether it is possible to hold them accountable for it. This agreement answers only the second question—yes, it is possible, but for now, it may be possible on only one company’s terms.

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 postcard 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.

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.

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.

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.

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.”

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.

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.

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.

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.”

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.