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

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

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

[Read: The flip-phone cleanse]

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

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

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

[Read: The extras are tired]

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

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

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

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

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

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

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

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

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

[Read: The AI industry keeps breaking the internet]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This conversation has been edited for length and clarity.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[Read: I am in cloud-storage hell]

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

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

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

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

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

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

[Read: Watch Apple trash-compact human culture]

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

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

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

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

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

[Read: America has a Pangram problem]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The air is bad this summer. Really, it’s historically disgusting. Smoke from wildfires has gusted over people’s homes, spreading haze and yellowing the sky. Last week, four midwestern cities—Chicago, Detroit, Milwaukee, and Toledo—reached the highest levels of particulate pollution they’ve recorded in two decades. For more than 100 million Americans across at least 20 states, the weather was grim: fumy with a chance of scattered lung conditions.

The air was bad like this two years ago, during that summer’s set of conflagrations—and let’s not forget the epic plumes of 2021, which turned the full moon over New York City a ghastly shade of red. Welcome to the new America: the land of burning throats.

In some respects, our skies have gotten cleaner in the past few decades; average levels of fine particulate matter have been cut nearly in half since 2000. But wildfires have been getting bigger and more frequent, and sending off plumes of air pollution that come and go in noxious episodes. The climate trend goes in only one direction, so this will be our future. That means we’ll need to have a better sense of when it’s safe to be outside, when it’s time to skip our morning stroll, and when we ought to keep our children home from camp. We’ll need to learn to live with smoke.

Enter the air-quality index: a weather metric for the age of wildfires. Perhaps you have never heard of the AQI or noticed it only last week, in news reports and weather-app alerts, when the smoke was getting thick. Chances are that you’ll be looking at it quite a bit in years ahead. But the AQI’s color-coded warnings, which run from wary orange (“unhealthy for sensitive groups”) to Grim Reaper maroon (“hazardous”), are useful only in the broadest sense: They may call attention to the risks of breathing smoke and remind you to be careful. As a tool, the AQI is inconsistent overall—its forecasts can be slow and imprecise, and its particulars don’t always have a bearing on direct experience. If wildfire events will get only more common, then this lousy measure must be fixed.

Globally, there’s no agreement on what constitutes unhealthy air—and no standard index of air quality. America’s version of the AQI was first created 50 years ago, as a way to standardize recording and reporting of pollutants and to clarify communications to the public. The system works like this: The concentrations of five pollutants—particulate matter, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide—are measured throughout the day at thousands of monitoring stations across the country; the readings are then combined and measured against safety thresholds that are based on regulatory limits. The AQI score is assigned such that a value of 100—which is supposed to mark the line between safe conditions and potential hazards—corresponds to standards set by the federal government.

Even just that fact can be a problem, Kevin Cromar, the director of the Health, Environment, and Policy program at NYU’s Marron Institute of Urban Management, told me. The regulatory limits for pollutants are, in theory, a reflection of those pollutants’ risk to human health. But that connection may be indirect, and the thresholds certainly aren’t calibrated perfectly for use by members of the public, who might use the AQI to figure out whether it would be safe to take a walk.

Another problem stems from the distribution of air-pollution sensors around the country. Where these are abundant, as in major cities, the reliability of any given reading might be high. But other parts of the country are more like sensor deserts, where AQI scores are interpolated from readings made in far-off places, and precision is reduced. The variation in uncertainty is not communicated to the public. AQI reports tend to look the same—a single number—no matter where you are.  

In some countries, air-quality scores are calibrated to reflect whatever mix of pollutants happens to be airborne on any given day. The United States is more single-minded: It focuses only on whichever pollutant has a concentration that most exceeds the standards, and disregards the rest. If, say, there was enough nitrogen dioxide on Monday for an AQI score of 105 and enough ozone for a score of 103, our air-quality reports would give only the former number: 105. Now imagine that on Tuesday, the nitrogen dioxide held at 105, while ozone levels dropped to 30. The resulting AQI report would look the same as it had the day before—again, 105—even if the air felt much better in the lungs of a person with asthma. Given this design, Cromar said, the meaning of the index may even be inverted from time to time, with a higher AQI corresponding to a lower risk of health effects.

This sort of issue would be easy to ignore under conditions like last week’s, when a single factor—particulate matter from wildfire smoke—dominated readings. But the mixed-pollutant problem points to a deeper issue with AQI: It can be hard to know what the numbers really mean. AQI reporting isn’t standardized across weather apps and news reports. In some places, you might see the measure as a total average for the day; in others, you’ll get a moving average (known officially as NowCast) that tries to show you how air quality is changing by the hour; still others, such as the basic weather app on my Google phone, offer up a mysterious hybrid of the two. Cromar told me that even people in his field—which is to say, the most sophisticated consumers of this sort of information—may be flummoxed by the range of AQI reports. “It’s a mess,” he said. “There’s just a lot of different information out there, and it’s unreasonable to expect the public to somehow make sense of all that.”

Then there’s the fact that different people may react in very different ways to the same concentrations of particulate matter, or the same readings on the index. In a smokier era, we’ll each have to learn for ourselves how the AQI numbers that show up on our phone relate to our sensations and experience. Cromar said that we’ll have to form new types of weather intuitions: Oh, the AQI is 135; I know what that feels like—I’ll put off my shopping ’til tomorrow.

Or maybe we could just get rid of AQI completely and have our apps provide us with raw measurements instead: a daily digest of pollutant concentrations, laid out in micrograms per cubic meter. At first, these data would be cryptic—who besides the biggest air-pollution nerd would know the difference between 35 μg/m3 of particulate matter (yellow-level risk, per the AQI) and 60 μg/m3 (red-level risk)? But then again, none of us was born with the understanding that 35 degrees Fahrenheit is cold and 60 degrees is pretty nice. I also had to learn, at some point in my adult life, that 35 percent relative humidity is very dry for where I live, whereas 60 is about average. This is true for every number in our weather apps: the daily highs and lows, the chance of rain, the UV scores, the pollen counts. You have to gain a feel for what they mean.

We’ve already formed internal models when it comes to measured temperatures, wind-chill factors, and the heat index. Now the weather’s turning, and it’s time to do the same for smoke.

Yesterday, OpenAI made an alarming disclosure: An assortment of its most advanced AI models, including one that has not yet been released, had autonomously broken out of the company’s internal systems and hacked into the databases of another tech firm, Hugging Face, to steal some information. OpenAI’s report seemed to augur the very sort of disaster that IT professionals have been warning about since last year, when Anthropic’s top models began demonstrating the ability to orchestrate and automate severe cyberattacks.

OpenAI’s models were not exactly trying to take over the world. The company says that it was running some routine evaluations in what is known as a “sandbox”—a walled-off environment that limits internet access, lest the bots simply search for the test’s answers. But these AI systems, including GPT-5.6 Sol, which is available to consumers, broke out of the sandbox and accessed the open web by exploiting a previously undetected vulnerability. The models discovered that the answers to this particular evaluation were on Hugging Face, a company that hosts and publishes many AI models and papers, and then broke in to steal them.

[Read: Chatbots are becoming really, really good criminals]

“We consider this incident to be an unprecedented cyber incident,” OpenAI wrote in a blog post, which also noted that the company is partnering with Hugging Face to investigate and address the issue. An OpenAI spokesperson pointed me to the blog post in response to a request for comment, and Hugging Face’s CEO, Clement Delangue, pointed me to an X post in which he wrote that the companies are collaborating and that “we strongly believe there was no malicious intent on their part.”

The first-order implications of the incident are the same that have arisen from countless AI-driven cyberattacks: Advanced agents are capable of more and more skilled hacking, and the internet is full of rickety and vulnerable code. Lax digital security may have been fine when hacking was a rare skill. But now the speed, scale, and sophistication of AI hacks mean that everything is vulnerable—tech companies, hospitals, banks, electrical grids, the military.

When Hugging Face first reported that it had been hacked last week, it noted that “autonomous, AI-driven offensive tooling is no longer theoretical.” That analysis stemmed from the company’s own investigation into the hack, for which it used a Chinese-developed AI model called GLM-5.2. That Hugging Face used this particular model actually underscores one of the major risks of this moment: GLM-5.2 is free to download, giving cyber capabilities to anybody with an internet connection and technical know-how. Defenses are not keeping pace with this new reality, and neither are AI companies: Prior to the release of GPT-5.6 Sol, a U.K. government agency tasked with evaluating advanced AI systems found universal jailbreaks—ways to hack the AI itself so it can take, for instance, criminal actions—during multiple rounds of testing. OpenAI said that it had mitigated those vulnerabilities, but the U.K. agency said that it expected future tests “to surface similar jailbreaks.”

But jailbreaks are clearly not even necessary for things to go haywire. The Hugging Face hack showed advanced AI systems deciding upon and then orchestrating a high-grade cyberattack on their own—the models working against, rather than according to, human instruction. Models are ruthless in their pursuit of a goal, sometimes taking disastrous shortcuts. Imagine, for example, a bot tasked with reducing the federal deficit that institutes an elaborate and hard-to-detect accounting gimmick, or a customer-service agent that issues absurd discounts in pursuit of customer-satisfaction ratings. In the case of GPT-5.6 Sol and the unreleased model, “all evidence suggests that the models were hyperfocused on finding a solution” for an evaluation, OpenAI wrote in its blog, “going to extreme lengths to achieve a rather narrow testing goal.”

[Read: Assume you will be hacked]

This unwanted behavior is a predictable and alarming result of how the entire AI industry is developing its models. The past year’s advances in AI coding and agents—that is, all the frenzy about Anthropic’s Claude Code and OpenAI’s Codex—have been the result of an overriding emphasis in AI training through a process called “reinforcement learning.” This involves giving AI models lots of hard problems—math proofs, coding challenges, what have you—and then providing positive feedback for correct answers and negative feedback for incorrect ones.

In many reinforcement-learning paradigms, the ultimate aim is just for the AI to arrive at the solution—it doesn’t matter how it does so. This is the AI equivalent of Mark Zuckerberg’s infamous dictum to “Move fast and break things” in Facebook’s early years. Last year, while I was working on a profile about Anthropic, one of the company’s researchers, Joshua Batson, told me that the new reinforcement-learning-trained models are getting “really good at solving coding problems, but they end up learning to solve them at all costs,” making them “bloody-minded.” More recently, Anthropic reported that its own advanced cyber model, Claude Mythos Preview, had taken “reckless” behaviors to accomplish various tasks and also, in one instance, had broken out of a testing sandbox. In that case, Mythos had been asked to do so—but the model then posted details about the exploit, unbidden, on the internet.

To be clear, this sort of AI “reward hacking” is a known problem that tech companies are putting lots of effort into addressing. But these incidents keep cropping up regardless; if anything, research suggests that they may become more common. Meanwhile, OpenAI, Anthropic, and Google DeepMind are under tremendous economic pressure to make their models more and more capable, which means that the bloody-minded reinforcement learning is all but certain to accelerate.

Anthropic has poached such an array of high-profile professors that it has become a punch line in academia. “‘I’m joining Anthropic’ is the new meme right now,” Subbarao Kambhampati, a computer-science professor at Arizona State University (who has not joined Anthropic), told us. This month, the AI company hired the chair of UC Berkeley’s department of electrical engineering and computer science, presumably to help build more capable bots. Perhaps more surprisingly, Anthropic has in recent weeks also picked up a Stanford economist, a theoretical physicist from the University of Maryland, and an analytic philosopher from UT Austin.

AI companies are turning into something like mini-universities in their own right. OpenAI employs top mathematicians and physicists—including one who studies black holes, and another who specializes in string theory. At least three computer-science professors joined Meta’s AI lab in late June. DeepMind, like Anthropic, is home to a crew of philosophers. And Anthropic’s recent job postings indicate an interest in hiring legal scholars and political scientists. It’s unclear exactly how many current and former professors are working at AI companies. Across these four firms, we found more than 80—the majority of whom are computer scientists. Some have left academia entirely; others are still working part-time at a university. That number is likely a significant undercount, because we don’t have access to internal data; it also doesn’t include the many professors who have started their own companies, those at other AI start-ups, and those working with the industry in a less formal capacity.

Particularly for AI researchers, these companies have a strong gravitational pull. “Much of the important research is being done in industry now,” Humphrey Shi, a computer-science professor at Georgia Tech who joined Nvidia as a vice president last fall, told us. As he sees it, “If you want to do something that really, truly matters, you probably want to join one of those entities.” Tech firms are making offers—including very enticing salaries—that are hard for academics to refuse. In the process, research that previously would have happened in the open is getting locked up behind closed doors.

For decades, universities were the center of AI research. The field itself officially began at a gathering of researchers at Dartmouth in 1956, and federal funding provided much of the field’s early support. In the early 2010s, Silicon Valley executives began to take AI’s commercial potential more seriously, and set out to hire the best researchers. In 2013, Google paid $44 million to acquire a start-up run by a trio of AI researchers from the University of Toronto. Facebook then hired Yann LeCun, an NYU professor, to establish the company’s AI-research lab; Uber poached some 40 Carnegie Mellon researchers to work on driverless cars. But for the most part, even as more work was being done inside of private companies, many of the newly hired academics at tech companies retained their professorships and established a culture of open research. “Researchers will be strongly encouraged to publish their work,” OpenAI wrote in its founding announcement. (Note the organization’s name.) This norm helped lead to the current AI boom: In 2017, scientists at Google published a research paper that was immediately of interest to OpenAI. Google’s innovation, called the “transformer,” is what the T in ChatGPT stands for.

As AI has taken center stage, Silicon Valley has intensified its efforts to recruit star researchers—and looked beyond computer-science departments. Philosophers help train tech companies’ bots to better interact with humans, and economists study the labor-market implications of AI. Compensation is only part of the draw. The current era of AI research requires massive amounts of computing power. Universities have only a fraction of the resources that Silicon Valley can provide, and the chasm has widened as the Trump administration has cut back on scientific funding. Anca Dragan, a UC Berkeley computer scientist who heads DeepMind’s AI-safety-and-alignment department, wrote to us that she was partly motivated to take the job to acquire “the data, compute, and budget access to make progress on safety at the frontier.” Some professors who remain in academia are also forming partnerships with frontier labs or starting their own AI companies, in part so that they can pursue their research without resource constraints. And in some cases, would-be star Ph.D. students are dropping out of their graduate programs—or skipping them altogether—to pursue careers in AI instead.

[Read: Someone finally wants to hire philosophers]

Although plenty of research is still happening within universities, many professors told us, the result is a flywheel: As more academics get sucked up by industry, the center of AI research moves further from academia, thus increasing the incentive for remaining researchers to leave. The exodus could have some advantages. Many of the professors currently at the labs are on temporary leave; some will likely return to academia full-time. When they do so, they’ll bring new knowledge about frontier research to their institutions. (Dragan is currently preparing to teach a small Ph.D. seminar on AI safety.) Scientists at AI labs might also innovate at a faster pace. After all, they don’t have to worry about applying for grants or waiting years for an article to go through peer review. “If some of the best research is being done in these companies, then I kind of want colleagues and faculty members to be there,” Chris Gregg, a Stanford computer scientist, told us. “You go where the best research is being done, and if that happens to be at a company, so be it.”

But when academics decamp to AI labs or spend more of their time working on their own companies, universities are left with fewer professors to teach the next generation of researchers. (Gregg said that students occasionally ask why courses are no longer offered. The answer is sometimes that the professor is on leave at an AI company.) When star professors leave universities, it becomes harder for students to do work that helps them stand out to prospective employers, Shi said. To differentiate themselves from other applicants, it helps to have worked with mentors who are doing consequential or cutting-edge research. Jennifer Chayes, the dean of the College of Computing, Data Science, and Society at UC Berkeley, told us she fears that as research concentrates in labs with proprietary models, it will be harder for people outside the labs to use AI models to advance science. “Computer-science departments at universities will survive this,” she said. “I don’t know if our innovation economy will.”

As the AI race intensifies, companies now decline to release much of their research for fear of giving away their competitive advantage. Both Gregg and Chayes said they hear from researchers who have gone to AI labs that they can’t publish the work they want to. “The top few AI companies share some things that are at the cutting edge, but it’s a very narrow slice of the potential AI literature that could exist,” Nathan Lambert, an independent AI researcher, told us. According to one study, when AI experts permanently transition from universities to companies, they publish roughly 65 percent fewer papers a year. Papers that do get published tend to emphasize progress on safety efforts and not frontier AI capabilities; corporate research can help fuel marketing hype. And with competition among AI companies escalating, it’s not only the research that is getting locked down, but AI models too. Last month, when Anthropic released a new powerful model called Fable, the company announced that it would invisibly degrade the model’s ability to do certain AI research. The move was positioned as a safety decision, but the resulting outcry from academia was strong. (Anthropic apologized and changed the policy.)

[Read: There’s never been a better time to study computer science]

Academics inside of the tech companies are more sanguine. Shi, the Georgia Tech professor who works at Nvidia, said the company is committed to open research, which is partly why he took the job. Meanwhile, Dragan, the Berkeley computer scientist at DeepMind, explained that publishing isn’t everything. “I find a much bigger priority than publishing is engaging in policy and regulation,” she wrote to us. We also reached out to spokespeople at OpenAI, Meta, and Anthropic for comment. Anthropic declined to comment; OpenAI and Meta did not respond.

The implications are potentially profound, not just for AI research, but for science more generally. In Silicon Valley, there’s a popular view that leading AI labs will end up as the primary drivers of science. “AGI has the potential to be the ultimate tool for advancing science and medicine,” DeepMind CEO Demis Hassabis wrote last week. Hassabis has previously said that an inspiration for his company is Bell Labs, the R&D arm of AT&T, which employed some 1,200 Ph.D.s at its height in the 1960s. Scientists at the lab earned at least 10 Nobel Prizes, and invented the transistor and the modern solar cell. Already, DeepMind has done Nobel Prize–winning research on protein-structure prediction, and the top models have already demonstrated impressive capabilities in mathematics research. On Sunday, a mathematician who works at Anthropic posted that he had used the company’s Fable model during the World Cup final to resolve a nearly 90-year-old mathematical problem.

But if scientific talent and computing power becomes concentrated inside private firms, Silicon Valley could also end up as a gatekeeper of science. Some are raising concerns: A group of academics recently published a declaration warning about “the increasing involvement of technology companies in mathematical research.” If left unchecked, they argued, the incursion of tech companies into research could affect “the scope and depth of mathematical research itself.” Instead of accelerating science, as the leaders of AI companies claim they will, they might end up suffocating it.

Physical credit cards should be a thing of the past. Over the past few years, touchless services such as Apple Pay have driven a boom in the digital-payment industry: Digital wallets now account for about half of global online purchases and one-third of point-of-sale payments. You would think that credit-card companies might abandon investing in cards that weigh down your wallet—but you’d be wrong.

Take, for example, the Platinum Card from Robinhood, introduced in March. Coated in 99.9 percent pure platinum, it weighs 27 grams, or more than half the weight of an average golf ball. “If you throw it on a dinner table, it's gonna make a ‘clang,’” Deepak Rao, a senior vice president at Robinhood, told me. The Platinum Card is just the latest addition to an ever-growing heap of lavish cards that speak to America’s appetite for flashy, metallic, and loud ways to pay.

American Express last year released a mirrored card so reflective that one card holder I spoke with told me that she uses it whenever she needs to touch up her lip liner in a fix. A similarly mirrored credit card made by the fintech company Bilt comes with its very own polishing cloth. Apple, meanwhile, provides a cleaning guide for its titanium card that recommends avoiding “abrasive objects.” Revolut, an online bank, offers a steel credit card that comes in a range of colors, including lavender, space gray, and rose gold. And JPMorgan Chase’s cards come in boxes that are themselves so intricate and high-quality that Brian Dean, a finance influencer, told me he keeps one on display on his shelf.

In 2022, the journalist Amanda Mull wrote in The Atlantic that metal payment cards were on the path to becoming luxury accessories. Today, that transformation seems complete. Opulent cards, marketed for years as status symbols, have been embraced by an entire commercial ecosystem of banks and fintechs trying to one-up each other, eager consumers, and influencers extolling cards’ virtues and ranking their weights with microscales.

Institutions are now designing cards with virality in mind: Some cards omit names and numbers so they can be filmed more easily, and others are proudly marketed by grammage. A card’s weight informs its so-called plunk factor, akin to the leather grade of a handbag or the sparkle of a diamond. Lewis Rubovitz, the chief strategy and marketing officer at CompoSecure, the leading manufacturer of metal cards in the United States, told me that most providers want their credit cards to be “heavy, heavy, and heavy.”

Such weight doesn’t come cheap: Industry experts told me that plastic cards cost issuers about $1.50 each to buy from manufacturers, whereas metal cards run companies about $15 on average. That price can surge into the hundreds or even thousands of dollars if those cards are made of metals like palladium, platinum, or gold. As cards get fancier, they typically become heavier. Whereas a plastic card weighs approximately five grams, cards made with stainless steel—the most popular material for metal cards—can weigh more than two or three times as much. A solid-gold card can weigh up to 36 grams, the equivalent of three AAA batteries.

Barclays makes a card plated in 24-karat gold, an inactive version of which is listed on eBay for $199. Robinhood-card holders can upgrade the company’s steel Gold Card to a version made entirely of 10-karat gold. Those cards can cost thousands of dollars to produce, depending on gold prices, according to Rao. American Express, meanwhile, has twice released limited-edition runs of cards made from melted-down Boeing aircraft, and Revolut coated a recent release in titanium of the grade used in an Audi F1 race car.

It’s all starting to get more than a little absurd. Leanne Fremar, JPMorgan Chase’s global chief brand officer, told me that the company has experimented both with different ways to incorporate metal into its cards, such as manufacturing versions with a metal core or veneer, and with finish techniques that make cards textured, ombré, or polished. Some efforts are years in the making. Robert Myrsater, the head of design at Bilt, told me he had dreamed of making a credit card with a reflective, chromelike finish since 2021, but he ran into a serious issue ahead of the January release of Bilt’s mirrored Palladium Card: It retained finger smudges. He tested out numerous layering treatments before finally just including the microfiber cloth that owners can use to keep the card looking fresh.

If your credit-card company doesn’t offer metal versions, third-party vendors have stepped in to do the job for them. Metal-CreditCard.com will “upgrade” plastic credit cards by extracting their chips and placing them into steel cards. Engraven Card, another company that turns plastic cards into metal, finds particular success in custom designs that make explicit references to wealth. “I’ve upgraded welfare EBT cards to say Millionaires Club,” Travis Jean, Engraven Card’s founder, told me.

The metal-card industry’s sales pitch is that a credit card’s weight carries genuine social currency. Online videos rank the various sounds metal cards make, ranging from “mid” to “like a flex,” when they’re plunked down on a table. “That matters to people: how it feels when it goes down at that restaurant bill,” Jessica Ling, the executive vice president of global advertising at American Express, told me. Metal-CreditCard.com’s founder, Vincent Torres, told me something similar: “The plunk factor of dropping a full metal card on the table, it changes people’s perspective of just who you are.” (Torres recently wrote a book titled How a Metal Credit Card Transformed My Life.)

The message appears to be working. Spencer Johnson, a YouTuber whose unboxings of cards have amassed hundreds of thousands of views, told me that videos on metal credit cards are “high-dopamine content.” Viewers “want a little taste of that just by watching somebody else open theirs, even if it’s not attainable for them,” he said. Rachel Tyers, another credit-card influencer, first made it big on Instagram when she posted a video explaining how to apply for the mirrored card from American Express. The video has 3.6 million views, and she’s since made videos of herself using scales to compare the weight of credit cards and testing which are scratch-resistant or show fingerprints.

Whether you really should be investing any of your sense of meaning and self worth into a three-inch slab of metal is, of course, a question all its own. But card sellers are tapping into a surge of interest in luxury goods. Demand for metal credit cards is on the rise alongside that for high-end jewelry, expensive homes, and premium airplane seats. Several people I spoke with speculated that the ubiquity of digital payment has only helped credit cards stake out new territory in the realm of the physically ostentatious. “It is a much more dramatic gesture to be at a table with a bunch of friends, and you’re plunking down a metal card over beeping your phone into the payment system,” Clint Henderson, a managing editor at The Points Guy, told me. Digital-payment services might be more convenient, but they can’t be held, flashed, or thrown down on a restaurant table. They certainly won’t score well on a drop test.

And so, the designs keep coming. Cards that light up, are made entirely of glass, and even contain jewels have already been manufactured for clients by CompoSecure. Revolut is hoping to soon bring its Ultra Card, which arrives encased in a transparent picture frame, to the United States. As for Robinhood, Rao has an idea for how to outdo his past products: a card in Vantablack, one of the world’s blackest coatings. “It’s going to be epic, but I haven’t figured out how to do it yet,” he told me.

There are worse things you could do than drive to a Parisian airport and mess around with a temperature sensor on the airfield—for only a few minutes! But why do that at all?

To make money, maybe. This is the scheme that some mysterious person (or persons) reportedly carried out in April to win a bet on the prediction-market platform Polymarket, which allows people to wager on the highest temperature in Paris on any given day, not unlike weather derivatives. The popular version of the story says that the shadowy figure warmed the sensor with a hair dryer—a photo showing this turned out to be AI generated—but it could have been anything. A lighter, a hand warmer.

By artificially boosting the number on the sensor, this person won about $20,000. The French police are investigating because you can’t trespass at the airport or tamper with its equipment, but Polymarket apparently didn’t take the winnings back. (It did change the airport that it relies on for Paris-temperature readings; the French government, however, blocked access to the platform this week.)

[Read: A technology for a low-trust society]

Over the past couple of years, plenty of concern has been expressed about how prediction markets may enable—and create the incentive for—insider trading. Some of the many people who have wagered on markets corresponding to when a war would start, when a regime would change, or even who would be at Taylor Swift’s wedding may actually have known the outcome before it happened. “Mention markets,” which allow participants to bet on words that a public figure might use in a speech, or how long the speech will be, can obviously be gamed by people close to the figure in question—just last week, ABC News reported that a teleprompter operator at the White House has made more than $100,000 by using Kalshi to wager on President Trump’s speeches. (“The White House has strict ethics guidelines that we expect all staffers and officials to follow,” a White House spokesperson told the outlet, while confirming that the employee had been placed on leave.)

The issues with insider trading are clear: It’s illegal, for starters, and the platforms ban it, anyway. (Although insider trading also arguably serves the purpose of prediction markets, which is to channel the “wisdom of the crowd” to forecast the event most likely to happen.) People can also easily identify and avoid the markets most susceptible to it. But what about the hair-dryer sort of trading, when an apparent outsider changes the course of events to cash in on a given outcome? “I think of it as probably a bigger issue or problem than insider trading would be,” Vincent Grégoire, a professor of finance at HEC Montréal, told me. The Parisian-airport incident didn’t hurt anybody, but future ones might. (You can imagine a bunch of ways in which someone might create a dangerous situation while, for example, attempting to delay an airplane.)

[Read: Insider trading is going to get people killed]

Some experts call this type of fraud “manipulation of the physical world”—a riff on market manipulation. I asked others for catchier ideas. Joshua Mitts, a professor at Columbia Law School, wasn’t sure that manipulation was the right word, because it has a specific definition for financial markets, but he still kept saying it accidentally as we spoke. I agree that the term is a little lacking. Manipulating the physical world is what I do every moment of every day as I breathe and walk. I would call this type of fraud “outsider trading.”

Outsider trading is also against platform rules. Polymarket and Kalshi both prohibit any type of market manipulation. When I reached out to Polymarket to ask about what had happened in Paris, a spokesperson relayed a statement to me from Olivia Chalos, its deputy chief legal officer, who wrote that Polymarket “maintains monitoring, review, enforcement, and reporting processes to address potential violations.”

But it’s obviously a bit of a whack-a-mole situation. The U.S. Commodity Futures Trading Commission recently proposed more federal rules for prediction-market platforms to ease the problem. One of the big suggestions was to prohibit the listing of markets that would “create perverse financial incentives”—for instance, markets on wildfires or a market on whether a famous ape named Little Joe would escape from a zoo (for a second time). Polymarket includes a note on that market explaining which law a person would be breaking if they attempted to free the gorilla themselves, and that they could receive a life sentence in prison for doing so. That may have been the first time a prediction-market platform has featured such a warning, the Bloomberg columnist Matt Levine noted in a May newsletter, “but I have not done a comprehensive survey and I doubt it’ll be the last.”

The experts I spoke with said that the most appealing markets for this kind of trickery would rely on a single number or simple, discrete outcome that a normal person could cheaply affect—the temperature reading in Paris, for example. Caleb Davies, an internet-famous prediction-market trader, highlighted another when he wrote about his experience losing thousands of dollars betting on Spotify’s streaming charts, which scammers had apparently figured out how to game by artificially juicing how many times a given song was played. Davies says the experience led him to an epiphany. He’s no longer just betting on the data, he said: “I’m also betting that nobody is going to mess with it.” And that’s a bad bet.

Davies posted a screenshot to X to show that he’d removed about $250,000 from his Kalshi account, and he says he’s stopped using it entirely. “I love prediction markets,” he told me. “I’d love to keep playing in them. But right now, both platforms are garbage, I think.” When I contacted the company for comment, the Kalshi spokesperson Jacki McGavick said that Kalshi “takes market manipulation or fraudulent activity as seriously as any federally regulated financial exchange” and is investigating the Spotify incident.

For the sake of conversation, Davies scrolled through Kalshi while we were on the phone, looking for opportunities for manipulation. Spotify charts were a good one, he knew. Rotten Tomatoes scores would be better (more money), though a little harder to game. (Nevertheless, New York magazine’s Matt Stieb figured out how to do it.) I pointed out to him that a market was open for the timing of this year’s first human case of screwworm in the United States. You could expose yourself to some infected livestock. But he wasn’t interested. “I think I’d just rather not make money than do that,” he said. “I’ve read about that—it doesn’t sound very appealing.”

[Read: It’s not gambling, it’s “girl math”]

Right now, the platforms—which are very much in advertising mode—carry a lot of markets that don’t have a ton of trading volume. They want as many as they can to attract new users and generate revenue. Also, a lot of them are funny: Most people probably recognize that it’s a waste of money to wager on whether Little Joe the gorilla will escape, but it’s fun to talk about. As more outsider-trading stories surface, though, people will likely be more hesitant to participate in markets that seem easy to rig. This problem, even more than insider trading, may start to limit what Polymarket and Kalshi can offer.

There are plenty of things the platforms could do to intervene in outsider trading, Joshua Della Vedova, an associate professor of finance at the University of San Diego’s business school, told me. They could limit the size of bets in markets in which the result would be easy to rig. They could look for suspicious trades using some of the same methods that detect insider trading. His preferred method looks for “excess accuracy.” If someone bets that an Olivia Rodrigo song is going to hit No. 1, that makes sense—but if they also specifically bet “no” on Drake and Charli XCX and Bad Bunny, then they’re demonstrating a suspicious amount of confidence. That confidence could come from having an inside line, or it could come from using bots to spike streams artificially. Platforms could also disallow markets that are resolved by checking a single number from a single source, he suggested. That would have solved the issue at the Paris airport, for example, and it’s not as if Polymarket couldn’t have used an aggregation of weather data, rather than just one reading.

Without some change, the platforms may continue to send ripples through reality—beyond just the goofy markets that they choose to list. A Wall Street Journal report found that Polymarket had been encouraging people to share videos with faked versions of their Polymarket dashboards, showing made-up earnings on imaginary bets that never happened. Weirder to me were the staged viral clips through which Polymarket and Kalshi looped themselves into the New York Knicks’ run to their first NBA Championship in more than 50 years.

In one of those clips, a purported Knicks fan outside Madison Square Garden yelled, apparently improvisationally, “My mayor Muslim, my bagels Jewish, my Christian Dior, Knicks in four.” Later, when it turned out that he hadn’t made this up at all—someone had posted it as a TikTok comment weeks before and parts of the rhyme were even older—and that the mic he’d yelled into had been Kalshi branded, the whole thing a Kalshi stunt, nobody seemed to care. The chant became real. It was everywhere.

The platforms pitch themselves as truth machines, which could mean a couple of different things. Are they finding it or making it? At some point, it may be difficult to identify the difference between reflecting our reality and creating a new one—or doing both.

The AI boom has showered some of the nation’s most prominent companies in market value. OpenAI and Anthropic are now the two most valuable private companies in the world. Google, Microsoft, and Nvidia have become larger than ever. But among the biggest winners has been Caterpillar, a purveyor of yellow trucks and cranes. Caterpillar’s stock has more than doubled in value over the past year, making the company worth six times as much as Nike. Its ascent has little to do with the construction vehicles (both real and toy) that it is known for. Instead, Caterpillar’s success comes from its giant gas-powered engines that are helping power the nation’s data-center build-out.

For all the attention given to improving model capabilities, the most important inputs to AI are not bits but atoms and electrons. To train and run its models, the AI industry needs to build. Data centers are incredibly complex technological and industrial operations—they require tech companies to erect power plants, build wastewater facilities, and set up all manner of electrical equipment, power lines, and advanced-cooling equipment. That calls for concrete, steel, silicon, glass, copper, and liquefied natural gas.

This data-center construction has long been frenzied, but now it is fast approaching an inflection point that is both awesome and alarming. The major players—Amazon, Google, Microsoft, Meta, and Oracle—are on track to spend more on data centers by the end of the year than they bring in from their operations. That means tech giants whose non-chatbot businesses bring in tens of billions of dollars every year will likely need to take on debt to keep paying for their AI ambitions. From the launch of ChatGPT, in late 2022, through the end of last year, these firms’ capital expenditures—most of which go toward data centers—well exceeded half a trillion dollars. The companies intend to spend a similar amount in 2026 alone. And next year, their AI investments will likely exceed $1.1 trillion, according to a recent report from J.P. Morgan.

[Read: Inside the dirty, dystopian world of AI data centers]

Silicon Valley has long enjoyed a straightforward advantage: Its products and services cost relatively little to make but generate tremendous revenues. But operating a data center is in many ways more like running a steel mill than a smartphone app. In particular, data centers require stunning amounts of electricity, especially as AI models have gotten more powerful. Five years ago, a standard data center might require 10 or 50 megawatts of power, enough for tens of thousands of homes. A year ago, data centers demanding a gigawatt or two of power were considered gargantuan. This week, Meta announced that it is more than doubling the size of its flagship AI data center, which will increase its peak power needs to five gigawatts. A data center proposed in Utah, if completed, will demand nine gigawatts of power. That’s several large cities’ worth of energy pulsing through a few big warehouses, all dedicated to bots.

Electrical grids typically don’t have enough energy to supply these megaprojects, and so the fastest way to bring a data center online is to build your own power plant. That’s where Caterpillar comes in: The company has an enormous number of back orders for its power equipment, as does every major natural-gas-turbine manufacturer in the world. In May, Elon Musk reportedly spent at least $1 billion to buy an energy company with a fleet of combustion turbines, likely to power his AI model, Grok.

Power is only one part of the challenge. A data center requires wiring together dozens of cutting-edge AI chips onto a refrigerator-size rack, then linking thousands of those racks in a building. Those chips run hot, up to 200 degrees Fahrenheit, and are typically cooled by some combination of water and industrial fans; get too close to one of the racks, and you may break a sweat.

OpenAI’s self-proclaimed goal of making “intelligence cheaper, better, and more abundant over time” really amounts to powering and cooling data centers as efficiently as possible—in other words, getting really good at HVAC. And then there’s the complex global supply chain of mirrors and lasers and rare-earth minerals that go into the chips used to train and run AI models.

As the AI industry pours more money than ever before into data centers, executives have begun sounding like they are in the construction business. OpenAI CEO Sam Altman has said that the biggest constraint on his company is “electrons”—that is, power. Musk, Microsoft CEO Satya Nadella, former Google CEO Eric Schmidt, and others have said the same. Meanwhile, Nvidia CEO Jensen Huang recently said that a lack of skilled “plumbers and electricians” will be the AI boom’s “hardest” bottleneck to overcome.

Still, Silicon Valley seems to have only just begun reckoning with what it means for its primary business operations to be in the material realm. You cannot vibe-code a data center. In perhaps the clearest sign of the AI industry’s desperation to keep building, Anthropic, the most safety-conscious firm, is spending $1 billion a month to rent a data center from Musk, who just a few months ago deemed Anthropic “evil.” Anthropic CEO Dario Amodei, for his part, has accused Musk of “disturbing negligence.” Meanwhile, the tech industry is struggling to quell a mounting AI backlash, which has largely taken the form of opposition to data centers. A few days ago, New York became the first state to institute a moratorium on new “hyperscale” data-center construction.

[Read: The AI backlash could get very ugly]

If anything, AI is poised to become even more like drilling an oil well. All the copper, silicon, electricity, and labor that go into building and running a data center are mustered to process and spit out “tokens”—in essence, words—that are the basic unit of AI. The State Department recently announced an international pact to bolster the AI supply chain, called Pax Silica. “If the 20th century ran on oil and steel, the 21st century runs on compute and the minerals that feed it,” Jacob Helberg, the undersecretary for economic affairs, said in the announcement. Financial markets and investors are already beginning to treat tokens as the raw inputs to modern civilization: Various firms are launching such markets for compute, akin to how you can trade oil, steel, and corn on a stock exchange.

By dominating the digital world, Silicon Valley has already exerted indirect control over the physical one. Today’s data-center build-out is a whole new beast. From the most concrete aspects—cooling, power, land—to the most abstract financial instruments, AI has morphed into a heavy industry.

Updated at 3:41 p.m. ET on July 16, 2026

There are two competing schools of thought over just how water-intensive AI is. In one, the technology is horribly thirsty. Data centers will exacerbate droughts throughout the country and “drain the great lakes,” as one popular Reddit thread puts it. The former Representative Marjorie Taylor Greene has taken up the issue, sarcastically posting at one point: “How dare the peasants complain about data centers stealing their water and driving the cost of electricity!!”

The other school, populated by Silicon Valley types, holds that “The Data Center Water Crisis Isn’t Real,” as an article in the right-wing publication Pirate Wires argued in December. The issue with water “is totally fake,” “insane,” and has “no connection to reality,” OpenAI CEO Sam Altman said a few months ago.

As with so many debates around AI, the arguments are not always presented in good faith. But unlike, say, the ambiguities over how much work a chatbot can or should be trusted to do, water use seems like it should be measurable, with clear facts supporting one side or the other. That is not the case. Does AI waste a ton of water? Not necessarily. Sometimes. It really depends. “We don’t have the real ground-truth numbers on data-center water use to put everyone on the same page,” Eric Masanet, a sustainability researcher at UC Santa Barbara who studies data centers, told me. Neither data-center operators nor utilities provide much insight; what public information exists can be contextualized, recontextualized, and distorted to suggest basically anything.

[Read: Generative AI is an engineering disaster]

Yet the water issue is tremendously consequential—not only for the well-being of residents in all of the counties where new data centers are popping up but also for the future development and regulation of AI itself. Just this week, when New York Governor Kathy Hochul signed the nation’s first statewide data-center moratorium, her office cited the “massive amounts” of water that hyper-scale data centers could require. In a drought-stricken county or during a heat wave, a data center may seriously burden local water supplies. Where water is abundant and utilities can afford new infrastructure, data centers may pose little issue.

Outside of water-stressed regions, “it’s not really a major concern for now,” Fengqi You, an energy-systems expert at Cornell, told me. “But as the sector grows, who knows?” Turning any particular case into a sweeping pronouncement produces little but heightened emotions and confusion. As ever, the truth is far more nuanced and conditional than activists or tech executives admit.

The problems start with the framing. How much water do data centers use? is not a particularly useful question. On a national scale, these buildings do not use an alarming amount of water. In 2023, data centers used slightly more than 17 billion gallons of water for cooling, as much as about half a million Americans collectively use in a year. This may seem like a lot out of context, but it’s less than a tenth of a percent of the total amount of water used by U.S. farms that year.

Things can look different at the county level. A Meta data-center campus under construction in Lebanon, Indiana, could, at its peak, demand 8 million gallons of water a day: This would be a drop in the bucket in New York City, but it’s more than double the peak demand of the town. Every data center demands a distinct evaluation, and the picture can quickly shift. Water use depends on the local climate, the local water supply, and the makeup of the regional electrical grid, as well as on the design of the actual data center. For Meta’s Indiana data center, city officials have said that planned upgrades mean there is “nothing to indicate” that they will run out of water. Meanwhile, in Newton County, Georgia, a local official has said that “we just don’t have the water” for all of the data centers trying to move in and that the county must “race” to improve local water-recycling facilities.

Rather than worrying about the quantity of water, it’s best to ask how a particular data center uses water. In general, water is always used for the same thing—to keep the computer chips inside these buildings cool—but there are many different methods for doing so. Broadly speaking, data centers can either expel the heat those chips generate with cooling towers, which evaporate water, or with air-cooled chillers or similar technologies that use electricity to vent away hot air. Think of cooling towers as human sweat, which passively cools us down as it evaporates, and chillers as car radiators, which use a chemical coolant and then blow air to physically push out the heat.

The AI industry has settled on the car-radiator approach for many of its biggest, most controversial data centers. They run water directly into the servers to pull away heat, cycle the water down through an air chiller, and then—crucially—reuse it. That’s how OpenAI’s Stargate data center in Texas, Meta’s Hyperion data center in Louisiana, Microsoft’s Fairwater data centers across the country, and many other enormous facilities operate for most of the year. Such closed-loop systems don’t lose water to evaporation, which is likely what Altman was referencing when he called concerns over water “insane”: Once these data centers have water, they don’t need more of it, in theory, because it’s never used up.

The real incentive for closed-loop systems is convenience. Building out local water infrastructure or tapping into a reservoir can take years, using groundwater is controversial (groundwater doesn’t replenish quickly, and using it can pollute the environment), and you can’t realistically bring water from Lake Michigan to your data center in dusty Arizona. Water availability can be a serious bottleneck to new data-center construction. A closed-loop system with air chillers bypasses these problems.

[Read: AI is taking water from the desert]

So why doesn’t every data center just use chillers? For starters, air chillers require substantially more electricity compared with cooling towers to expel the same amount of heat. “Can data centers use zero water? Yes, that’s very easy,” Shaolei Ren, an AI and sustainability researcher at UC Riverside, told me. “But the downside, the cost they have to pay,” is using more electricity—10 to 65 percent more, by various estimates. One nine-gigawatt facility being considered in Utah would require nearly as much power as New York City.

Data centers’ almost surreal power demands are largely being met by combustion turbines, many of which are built by the tech companies themselves (to avoid waiting on upgrades to the local grid). And generating electricity—including from these private power plants, which burn fossil fuels—needs water too. In 2024, Meta’s “indirect” water consumption—referring to the water used during power generation—was 19 billion gallons. That’s 23 times its direct water consumption, and most of it went to data centers.

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

Few industries consistently report indirect water use, in part because many of the estimates are flawed. Indirect use comes from averaging the water footprint of all the power sources on a regional grid—which includes hydroelectric plants that consume huge amounts of water due to evaporation from dams and lakes. In other words, indirect water-consumption figures can arguably under- or overestimate any individual plant’s contributions. Paradoxically, the AI industry’s decision to avoid using water to dissipate heat from inside its data centers might expand some facilities’ overall water footprint. “If you use water to make your cooling more efficient on-site, you will use less electricity,” Jonathan Koomey, a data-center and sustainability researcher, told me. “It’s not a simple matter of water use bad on-site.”

It might be possible to design data centers that use both less electricity and less water. In cooler climates, for instance, data centers can pump in outdoor air; this is what Amazon does at its mega data center in northern Indiana. In summer, spraying small amounts of water into the outside air can bring down the temperature enough to do the same. Data centers are also beginning to experiment with allowing the chips to run at higher temperatures, which reduces the overall cooling demands. And, of course, the rather obvious way to build data centers that put less strain on the environment is to opt for renewables—which consume almost no water—instead of natural-gas turbines.

Until these approaches are the norm, people will understandably worry about the effects these sprawling facilities will have on their communities. Assessing how any data centers will affect nearby reservoirs ultimately requires the scarcest resource of all when it comes to these facilities: accurate and precise information. Tech companies want data centers up and running quickly, so they contract with shell companies that negotiate behind closed doors and under nondisclosure agreements. Residents might find out about new construction only after shovels are in the ground.

Already, rising temperatures and prolonged droughts are out of any individual’s control; now a big industrial facility arrives, under the cover of night, that may or may not exacerbate many problems caused by the climate crisis. This sense of confusion goes a long way toward explaining why data centers’ environmental impacts have become one of the key battlegrounds in the AI backlash—the primary issue, in many cases, is not lack of water so much as lack of control.

This article originally referred to a facility under construction in Utah. In fact, that proposal is still being considered, and construction has not begun.

In an impressive challenge to the idea that no publicity is bad publicity, Anthropic is now promoting itself with images of disaster, death, and human suffering. The AI company’s latest ad, which debuted during a World Cup match last week, is a perplexing 90-second artifact. The commercial opens with footage of a house engulfed in flames and the crackling sound of burning wood. From there, a series of dark images—rows of gravestones in a cemetery, a homeless couple sleeping on the street, laborers working in a West African granite quarry—flashes across the screen. These stills are accompanied by a series of voice-overs: “Can AI be trusted?” “Who’s going to hit the brakes if we need to?” “How do we really ensure that what we’re aiming to achieve really does benefit the majority of people?” The questions are left unanswered.

Halfway through, the ad moves from dystopian film trailer to feel-good corporate ad. We see a photo of a mother sitting beside her daughter as a voice speculates that AI will allow her to be a better mom. Later, a medical worker uses a laptop covered in Anthropic stickers. “Maybe it’ll cure some great things,” a man says. A nature montage plays—there’s a humpback whale, grassy hills, and people hugging.

Finally, text flashes on the screen: “There’s hope in hard questions.” In the background, we see the ocean. And then Anthropic’s tagline: “Keep thinking.”

The commercial is tone-deaf. It’s one thing to mention the possibility of hitting the brakes on AI progress; it’s another to overlay the question on an image of dead Americans. The video is also just confusing. Does Anthropic think that AI is going to set the world on fire and leave us jobless, or that its chatbot, Claude, will lead us to a more human future where we spend our days watching whales by the sea? The company’s answer seems to be: We don’t know. The video has been mocked, obviously. Some people likened it to hypothetical ads by oil companies professing concerns about the environment. The head of one tech-media company posted, “I actually like the tagline but we don’t need the shots of *checks notes* burning houses and graves!” Meanwhile, OpenAI’s Sam Altman—who has a self-interest in undercutting Anthropic—chimed in to call the ad “satire.”

This is not the first time that a company has botched its advertising around AI: OpenAI has had its own share of embarrassing ads. By now, it seems like a rite of passage. Two years ago, Apple ran a commercial depicting musical instruments and art supplies being crushed by a hydraulic press. The ad was supposed to show off just how much creativity could be compacted into an iPad, but instead it seemed to symbolize generative AI’s bludgeoning of the arts. Apple subsequently apologized. During the Olympics that summer, Google ran an ad in which a young girl wants to write fan mail to the track star Sydney McLaughlin-Levrone—so her dad fires up Gemini and asks the bot for help. The ad was highly unpopular, and Google pulled it from TV. If anything, Anthropic has had a comparatively good run until now. During this year’s Super Bowl, the company aired a commercial dunking on OpenAI for putting ads in user conversations, which even Altman said made him laugh (despite his qualms with how the ad presented OpenAI).

[Read: Watch Apple trash-compact human culture]

For all the criticism, Anthropic’s latest commercial captures the company’s ambivalence about its own existence. Here is a leading AI company worth nearly $1 trillion arguing that the very technology it is racing to commercialize may well imperil the world. “We founded Anthropic because we believe the impact of AI might be comparable to that of the industrial and scientific revolutions, but we aren’t confident it will go well,” the company wrote in 2023. In recent years, Anthropic has made a show of positioning itself as the ethical AI company. Earlier this year, it attracted the ire of the Trump administration when it pushed back against the Pentagon’s demands for unrestricted access to its AI models over concerns that the technology could be misused.

The new advertisement is part of a broader campaign Anthropic is running to address the “hard questions” raised by AI’s progress. In an email, an Anthropic spokesperson pointed me to a series of blog posts about the initiative: This spring, the company traveled across the country to better understand the public’s views about AI. In the video’s description on YouTube, Anthropic says that every question in the ad comes from a person that the company spoke with. (The spokesperson declined to comment further.)

The idea that Anthropic is worried about AI’s negative impacts and wants to hear from the public is certainly better than the alternative. The trouble, though, is that as Anthropic has matured, the company’s constant messaging about its anxiety over the future of AI has become less palatable and, occasionally, downright maddening. “Anthropic is at war with itself,” my colleague Matteo Wong wrote in January. Since then, the company’s valuation has soared. As it develops more advanced AI models and races toward a public offering, the contradictions are becoming more difficult to square. However sincere Anthropic may be—Wong noted after several visits to the company that its employees can be “almost pathologically earnest”—it’s no surprise that people are wary of the world’s most valuable private company asking the “hard questions” about AI.

[Read: Anthropic is at war with itself]

The same tension is at play across the industry. OpenAI was founded as a nonprofit with a mission to “ensure that artificial general intelligence benefits all of humanity.” In the years since ChatGPT was first released, the company seemingly has prioritized financial gain instead. By now, it feels pointless for Anthropic and the rest of the industry to simply pose questions about AI ethics and safety. These questions are already consuming the public’s imagination amid a mounting AI backlash. What matters is the answers. And on that front, AI companies have less to offer. Perhaps the commercial’s tagline provides a helpful clue: For now, Anthropic will keep thinking.

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

As they scramble to keep their systems online, AI companies are making things expensive for the rest of us. Large language models such as ChatGPT and Claude are so resource-hungry that tech companies may be purchasing 70 percent of the world’s supply of high-end computer memory, causing a shortage. As a result, the prices of computer memory and storage are skyrocketing: Hard drives that I bought for my reporting two years ago for $350 each were $800 when I checked two weeks ago, and are now out of stock. The prices of some laptops have gone up as much as 50 percent, and low-cost computers are being hit the hardest. Affordable entry-level computers may “disappear by 2028” according to one forecast. And the memory shortage is expected to continue for years.

The memory is being put into data centers, which tech firms are expanding at incredible speed. They are planning to multiply total U.S.-data-center capacity by a factor of eight over the next few years. The demand for electricity at these sites is already so great that some companies are repurposing jet engines to power them.

The problem is not simply that AI is being deployed so widely or quickly. Other computer technologies have seen similarly massive growth without triggering such a large spike in electricity or a shortage of computer components: Video and music are now streamed around the globe, accounting for many terabytes of internet traffic daily; the smartphone boom required the manufacturing of billions of devices that are now transferring huge amounts of data; billions of household devices are also now part of the Internet of Things; and whole industries have moved their operations to cloud software, which is hosted not in the sky but in, yes, data centers.

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

The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups. They want to see that the cost of adding each new user decreases over time, so that the company can support millions of users and make increasing profits. This is achieved partly through the careful engineering of computer systems that can efficiently handle more users who want to post photos, hail Ubers, or stream music.

With generative AI, the work of building efficient, scalable systems has not been done. And the problem is exacerbated by the ever-larger generative-AI models, which have grown from 175 billion parameters in 2020 to more than 1 trillion today, according to independent estimates (the actual sizes of the models powering products such as Claude and ChatGPT are secret). The large in large language model should not be a selling point. But the industry’s observation that bigger models tend to outperform smaller ones has given rise to a totemic belief in “scaling laws” that suggest any problem can be solved by simply making models bigger. “Maybe with 10 gigawatts of compute, AI can figure out how to cure cancer,” OpenAI CEO Sam Altman wrote on his blog in September.

Yet the returns are diminishing. The bigger an AI model is, the less it improves with each added parameter, and so it must be made bigger at a faster rate just to sustain steady progress. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. Even outside the world of software, it’s hard to find a comparable example, given that economy of scale is the principle that has made light bulbs, cars, and clothing so affordable. By economic and engineering measures, generative AI might be the worst technology ever deployed.

[Read: Welcome to a multidimensional economic disaster]

But with the massive investment behind the current bloated approach, there may not be much will to change. Ilya Sutskever, a co-founder and former chief scientist at OpenAI, said in a November interview that companies take the brute-force approach “because it gives you a very low-risk way of investing your resources.” It’s harder, he argued, to invest in research that would reengineer a product currently accruing trillion-dollar valuations. Those who suspect we are in an AI-driven bubble economy have pointed out that the profitability of these companies remains an open question, largely because of the high cost and inefficiency of the technology.

Efficiency is a core principle of computer science. One of the first things undergraduates learn is that writing a program that sorts a list of 50 words is easy. But if you give that program 50 million words, it will likely run out of memory or take hours to finish. Much of computer science is learning the clever coding techniques that prevent this from happening. Many of these techniques take advantage of repeating patterns in the data so that as the program receives more input, it takes less time or memory to process each additional bit. Such efficiency is one reason that modern smartphones and computers are so capable and affordable. This is called logarithmic scaling, and it looks like this when you graph it:

Large language models do not scale logarithmically. When they’re given more words to process, they get slower and use more memory—the time and resources increase faster as the input grows. In technical terms, LLMs scale quadratically. Any computer-science student knows that this is very bad.

Epoch AI, an organization that tries to determine the costs of operating AI models, published a graph last year, which is reproduced here with permission. It shows the exponentially increasing costs of serving more “tokens”—the words users type to chatbots—with several public AI models.

AI does not have to be built this way. Traditionally, the goal of AI was to solve problems in ways that simulated human mental processes. Researchers observed their own thinking and tried to implement their mental habits in code. This approach has mostly been abandoned, partly due to the difficulty of discerning and articulating the rules of human thought, but it did have the benefit of consuming far fewer resources and data.

Today’s approach to AI doesn’t try to describe the rules of human thought; instead, it gives a computer millions of examples to imitate. The huge quantity of examples is one reason that large models can perform better than small ones when generating language, images, and music—they have more material to draw from. Some researchers want to bring back the old, more efficient approach and combine it with the modern approach, but so far these projects have not drawn nearly as much attention or funding as the models that power chatbots.

Chatbot companies are aware that their products are inefficient. Some have found techniques for improving performance, but they have not yielded significant gains. Occasionally, companies claim to have made breakthroughs—Anthropic CEO Dario Amodei has called them “compute multipliers”—but they are usually described in vague terms, and there is no evidence that the basic problems of quadratic scaling and exploding model size have been overcome. (Anthropic declined to comment on the record when I reached out to ask about this.)

Some researchers are working on extremely small models that require fewer data and less computing power. I spoke with Alexia Jolicoeur-Martineau, an AI researcher at Microsoft who independently designed one of these, and asked her about the industry’s brute-force approach. “It’s a bit insane,” she told me. “At some point you have to learn to be a bit more efficient.”

Last year, Jolicoeur-Martineau won a $50,000 prize for her paper on a “tiny recursive model” that doesn’t consume huge quantities of computing resources. “The idea that one must rely on massive foundational models trained for millions of dollars by some big corporation in order to achieve success on hard tasks is a trap,” she wrote. Her model isn’t a substitute for an LLM—it’s designed to solve logic problems in fields such as biology and electrical engineering, rather than generate language—but it can do some of the tasks that much larger AI models are currently being used for.

Yet we seem stuck with LLMs, perhaps because they have been so aggressively marketed. They are now being added to everything, whether you want them or not. In 2024 and 2025, they were integrated into both Windows and MacOS, which means that more computing power is now needed to run a basic personal computer. Smartphones are also being sold with upgraded hardware as companies anticipate new AI features. Inefficient AI is also being added to common programs such as Adobe Photoshop and Microsoft Word, meaning that computers need to be more powerful to run this software.

[Ted Chiang: No, artificial intelligence is not conscious]

This is all especially bad because computers are no longer improving at the rate they used to. Since the 1950s, manufacturers have learned to make microchips steadily faster, smaller, and cheaper, a trend known colloquially as Moore’s Law. But in the past few years, components have gotten so small that manufacturers have run into molecular-level limitations on shrinking them any further, which has slowed progress significantly.

Instead of shrinking components, manufacturers have been focused on developing new hardware that is tailored to AI. This has provided occasional incremental improvements in performance, but none has come close to keeping up with the exponential curve of AI’s increasing demands.

Ultimately, inefficiency may be of little concern to the people within the tech industry who believe that they are replicating intelligence itself. There is an almost-religious conviction among many in Silicon Valley that something mindlike could arise from LLMs, which are ultimately just statistical language-generating software—this, despite the software’s inability to recall basic facts, its lack of common sense, and its complete dissimilarity to a biological brain. Even Yann LeCun, one of AI’s “godfathers,” told The New York Times recently that “LLMs are not a path to superintelligence or even human-level intelligence.” But the mythological lure of AI is so strong that many engineers believe that nothing should stand in their way. Not even the basic task of writing efficient software.

If Julius Caesar had debuted this year, William Shakespeare might have been accused of writing it with AI. A certain suspicious rhetorical device appears again and again in the play. It’s in Act I, Scene ii: “The fault, dear Brutus, is not in our stars, but in ourselves.” In Act III, Scene ii: “Not that I loved Caesar less, but that I loved Rome more.” And later in that same scene: “I come to bury Caesar, not to praise him.”

These famous lines include what has become perhaps the best-known tic of AI writing—a sentence that tells you what the subject isn’t as well as what it is: It’s not X; it’s Y. Once you start noticing the construction, you see it all over the place. In one version, the Y is additive: It focuses, intensifies, or expands on the X. An annual review by Citizens Financial Group reported that growth in its private-banking division was “not just a win for the private bank—it’s a win for the entire enterprise.” In another variant, the Y supplants the X as the preferred descriptor. “The target was never a man. The target was the truth,” Michael Flynn, a former Donald Trump adviser, wrote in a March X post.

Then there are constructions like No A, no B, just C, which especially seem to crop up in AI-generated fiction. Lines such as “No bag, no things, no armor, just me” helped to fuel accusations of AI writing in the horror novel Shy Girl, which was pulled by its publisher this year. (The book’s author denied using AI to write it. Citizens Financial Group has previously said that its communications team “leverages the technology in a number of areas.” Flynn did not respond to a request for comment.)

The prevalence of this device isn’t just anecdotal—it’s measurable. (Sorry.) Barron’s reported that its appearance in corporate communications more than quadrupled from 2023 to 2025. Researchers at Pangram, which makes an AI-detection tool, estimate that Not just X but Y sentences appear three times as often in AI writing as they do in human writing. Elyas Masrour, a founding engineer at Pangram, told me that all of the major chatbots—including ChatGPT, Claude, Gemini, and various open-source models—rely on it to varying degrees.

Many other well-known chatbot tells—such as the usage of delve—have come and gone as AI companies honed their models and worked out kinks. Last fall, ChatGPT became obsessed with goblins and gremlins, prompting another intervention: OpenAI retired ChatGPT’s “nerdy” personality, whose affinity for mythical creatures had apparently infected its other models. Yet It’s not X; it’s Y has shown no signs of abating.

Before ChatGPT came along, the construction was obscure enough that it didn’t really have an agreed-upon name. Now there’s a scramble for what to call it. Terms from academia, such as antithesis and metalinguistic negation, capture some forms of the construction but not others. In an email, Laurentia Romaniuk, a product manager for model behavior at OpenAI, referred to it as “contrastive phrasing.” Despite its clunkiness, the most popular name I’ve seen is “negative parallelism.”

When deployed judiciously, negative parallelism can be punchy. But ChatGPT turns to it too often, Romaniuk acknowledged, which can feel formulaic. So the company is working on ways to broaden the chatbot’s repertoire. In the meantime, she added, users can try giving ChatGPT “custom instructions.” On a Reddit forum about AI writing, users trade tips for scrubbing negative parallelism from chatbots’ writing. One suggested pasting Claude’s output into another AI chatbot and telling it to act as a copy editor who has a strict ban on “negative pairings” such as “it wasn’t X, it was Y.”

One obstacle to a more comprehensive fix is that no one seems to know for certain why AI models are so enamored with negative parallelism in the first place—maybe not even the companies that created them. (Anthropic and Google did not respond to my requests for an interview.)

The simplest theory is that humans trained them that way. Large language models are built by first identifying patterns in unfathomable quantities of human-written text: books, academic papers, patent filings, and especially the internet. Negative parallelism was, of course, present in the initial training data. Shakespeare aside, there are lots of famous examples: In the 1960s, the legendary football coach Vince Lombardi popularized the saying that “winning isn’t everything; it’s the only thing.” In the 1990s, a frozen-pizza brand’s commercials insisted: “It’s not delivery. It’s DiGiorno.”

But the training data also included lots of bad writing that AI companies don’t want their chatbots to mimic, Tuhin Chakrabarty, a computer-science professor at Stony Brook University who studies AI writing, told me. So they also undergo “reinforcement learning,” a process by which human reviewers grade the models on their responses. Through trial and error, chatbots are guided away from inappropriate responses (making stuff up, giving illegal advice, insulting the user) and toward those rated helpful. Chakrabarty said that it’s plausible that human reviewers tended to give high marks to responses that included It’s not X; it’s Y. That could be because negative parallelism gives the impression of nuance and insight: The AI seems to be reasoning its way from a subpar descriptor to a more apt one.

That still may not be enough to explain just how prevalent the construction seems to be across the major AI models. Several experts I talked with pointed me to another, even weirder explanation.

Although chatbots have advanced dramatically in their research and reasoning capacities, they are still fundamentally text-prediction machines. They generate answers one “token”—or chunk of text—at a time, based on what has come before. Each successive word choice factors in both the statistical likelihood of that word coming next in a sequence, based on patterns in the original training data, and the likelihood that it will lead to a highly rated response overall. In other words, the models are always seeking a balance between the clever word choice and the obvious one.

When a chatbot uses negative parallelism, according to this theory, it’s essentially hedging between the two. Once it has started a sentence whose function is to characterize something, the path of least resistance is to say first what the thing isn’t (X), and only then what the thing is (Y). Put another way: For a sentence that begins with “This is,” following it with “not just” is both more likely and safer than the many options for how to directly characterize its subject. And after “This is not just,” the rest of the sentence gets easier too. The next word can be X—the boring, obvious descriptor that gets negated—which in turn sets up the final choice of Y, the somewhat punchier descriptor.

Even if researchers could figure out exactly why chatbots embrace negative parallelism, there’s another factor that could make it very hard to fix: “When something gets into these models, it’s very hard to pull it out,” Masrour, the Pangram engineer, said. That’s because one of the main ways that AI models have continued to evolve is by training on text generated by other bots. That AI text is presumably replete with negative parallelism, which further bakes it into the newer model. Now consider that a growing share of the writing on the internet is also AI-generated. This, too, becomes training data for future generations of AI.

On top of that, some AI labs are also using AI instead of, or in addition to, human reviewers in the post-training process, Chakrabarty said. Without intervention, there’s a risk of “model collapse,” in which AI reinforces its own biases to the extent that it loses touch with the human data that were meant to ground it. “It’s a very vicious loop,” Chakrabarty said. “There’s already negative parallelism in the text, and then AI is preferencing negative parallelism—it comes to a point where it just cannot write without that.” AI language is eating its own tail.

Chatbot clichés might be grating, but there’s an upside to them: They make AI writing easier to distinguish from the human variety. Masrour said that although the AI writing’s specific markers keep changing, it isn’t actually getting any more difficult for Pangram’s software to detect. The stubborn persistence of constructions such as negative parallelism may be one reason.

The trade-off, for human writers, is that a once-potent rhetorical device is now a cliché that makes you sound like a bot. That has put some people in the awkward position of insisting that they’re not using AI—that’s just how they write. Before you mock them for it, consider that you too might soon find yourself talking and writing more like a machine: A recent study by researchers in Germany suggested that AI’s writing tics are now cropping up more in spontaneous human conversation. If that continues, maybe negative parallelism will eventually lose its status as an AI-writing tell after all. The fault, dear readers, will be not in our chatbots, but in ourselves.