Driving after dark used to be a haven. Late at night, there’s no rush-hour traffic, just the meditative hum of the passing miles. But these days, my eyes can’t take it anymore. Even on a lonely road in the middle of the night, I can’t seem to escape the glare of obnoxiously bright headlights. A pickup truck tailgating me blinds my rearview mirror with searing headlights. Even at a distance—and even when without the brights on—the beams of a vehicle in an oncoming lane make me instinctively squint. America’s roads are now full of tactical-grade headlights, and no one is happy about it. Just look at any of the viral screeds in the Reddit forum “r/fuckyourheadlights.”

Other people’s lights are a pet peeve nearly as old as the automobile: Everyone’s beams are too bright, too aggressive, and purposefully pointed into your eyes. But the problem really is worse than ever, and that’s because of the LED headlights that have taken over car design during the past decade or so. Not only can they crank out more lumens than the halogen lamps of old; their light is also sharper and bluer, which makes it feel like an assault on the eyeballs. Car companies have an incentive to install bright headlights that make drivers think Oh man, you can see everything, Jonathan Elfalan, the director of vehicle testing at Edmunds, told me. The wincing driver in the opposite lane isn’t their problem.

Across the world, high-powered headlights are far less menacing. That’s partly because of America’s obsession with crossovers, SUVs, and pickup trucks. New cars are taller and bigger than ever before, which means they are blasting light everywhere but down at the road ahead. “There’s a pretty good chance that the car behind you is shining its headlights right into your mirror,” Sean Tucker, a managing editor of Kelley Blue Book, told me. Still, other countries have long had something we don’t: high-tech headlights that can brilliantly illuminate the road in front of them without simultaneously burning another driver’s retinas. Although American cars are loaded with technology—plenty of new models now come with self-driving features and AI assistants—we’ve been missing out on a simple feature that would ease the problem of blinding headlights.

This technology is called “adaptive driving beam,” or ADB. Adaptive beams scan the road ahead of you and adjust accordingly: On a curvy stretch, the lights will track the road rather than shining straight ahead. They also can detect cars coming your way and dim just the light that’s pointed right at your fellow motorists. This is possible because an LED headlight is not a single light bulb that’s on or off but is made of a multitude of individual pixels. Adaptive beams have been popular in Europe for more than a decade and are in use in Asia and Canada. In the United States, just a handful of vehicles have them.

Blame the often strict and strange world of American car regulations. Decades ago, the National Highway Transportation Safety Administration mandated that cars need to have separate low and high beams. Adaptive beams—which are variable in nature—don’t fit into that binary. Congress finally amended the rules in 2021, a move meant to legalize the better method. That didn’t happen. NHTSA got to write the new regulations, and instead of adopting the international standard, it drew up a separate set of stricter rules. (An NHTSA spokesperson pointed me to the stance that the agency outlined a few years ago, which says that Europe’s test for adaptive bulbs lacks the “objective performance criteria” necessary for approving car tech in the U.S.)

The result of this confusion is that although new cars in America have the technology in place to shine less blinding beams—possibly activated via a simple, over-the-air software update, Tucker said—that’s illegal. This gets even more absurd when you consider how simple fixes have solved previous headlight woes. Even new entry-level cars come with an auto-dimming feature to deactivate the brights when another car approaches, minimizing breaches of headlight etiquette.

Instead of turning on technology that already exists, the pathway for car companies to fix American headlight glare involves reinventing their smart headlights. In 2024, Rivian put the tech into its pickups and SUVs and became the first automaker to activate compliant adaptive beams in the U.S. That happened only after Rivian redesigned the entire electronics-and-computing setup in its vehicles, a move that gave the company enough control over the LED headlights’ pixels to satisfy American regulators. “The standard is genuinely demanding,” Carlos Montes Relanzon, Rivian’s senior manager for lighting systems, told me.

The solution is powerful, if eerie. When I test-drove Rivian’s R1S SUV up the California coast last summer, I couldn’t stop staring at the intermittent hole in my headlight beam—a dark spot that would appear as if part of the lamp had burned out, then vanish. At first unaware that Rivian had rolled out the feature, I wondered whether the car might be defective. When I finally realized what the SUV was up to and began to track the tech’s performance, I was reassured by the fact that the Rivian could recognize other cars and turn down the lights while they were still several seconds away, rather than waiting until an oncoming driver was right next to me.

The rest of America’s car industry is slowly coming around. Last year, Tesla introduced adaptive beams in its updated Model Y crossover. And last month, Audi announced that it would put adaptive beams in the 2027 Q9 SUV, set to debut in the U.S. later this year. An Audi spokesperson told me that the company is holding out hope that the NHTSA will come around and agree to adopt the international standard. For now, other car companies will also have to suck it up and sink money into rebuilding adaptive headlights for their American cars. (I asked Ford, Chevrolet, Toyota, and Mercedes about when the automakers might introduce adaptive bulbs, but did not receive more information.)

Unless America suddenly lets carmakers turn on the tech that’s already sitting dormant in plenty of vehicles, today’s glaring lights are going to stick around. Cars now stay on the road longer than ever—13 years on average—so even if every new vehicle today began offering adaptive beams, the journey to replace old cars with new ones that have friendlier headlights would be long. Cool feature or not, adaptive beams are unlikely to persuade many drivers to trade in an older vehicle for a new one. After all, they improve the lives of other drivers more than they help car owners themselves.

This becomes clear when you’re the highway Good Samaritan. As I meandered through the blackness of Route 101 in the Rivian, passing roadside outposts such as King City and Soledad, my vehicle auto-dimmed its lights for one oncoming driver after the next. Mile after mile, their lights still kept me squinting.

Updated at 4:20 p.m. ET on June 5, 2026

Elon Musk is about to set in motion a chain of events that will reshape the global financial order. For starters, when SpaceX formally goes public next week, he is all but guaranteed to become the world’s first trillionaire. His rocket company is targeting a valuation of $1.77 trillion, which would make it one of the 10 biggest companies in the world—bigger than Meta, Walmart, and, for that matter, Tesla.

All of this activity is less about colonizing Mars and more about providing the infrastructure for the AI boom: Musk wants to use his rockets to launch data centers into space, where there is abundant solar power to harvest. His rush to tap into the public markets seems to be pushing the other AI behemoths to do the same. On Monday, Anthropic filed confidential documents to begin the process of going public as early as this fall. And OpenAI is reportedly preparing to do the same any day now.

Anthropic and OpenAI, too, will likely be valued at $1 trillion or more, dwarfing JPMorgan Chase and Exxon Mobil. These will be three of the largest initial public offerings, or IPOs, in history—and all three may hit the stock market in the span of just a few months. It’s hard to overstate the effects this rapid succession of gargantuan IPOs will have on the American economy—think of it as dropping three boulders, one after the other, into a kiddie pool.

These companies are not going public because they are moneymaking machines. Consider that SpaceX, in the lead-up to its IPO, revealed that while it is seeking a nearly $1.8 trillion valuation, the company has lost $4.3 billion in just the first three months of 2026. OpenAI is also unprofitable, while Anthropic might barely post its first profitable quarter ever at the end of June. Yet the trio are set to become among the most valuable companies in the world because of a promise of unprecedented riches on the horizon. In a Securities and Exchange Commission filing, SpaceX wrote that in the long run, AI will eat the economy, and so its total revenue could be, in essence, equivalent to most of the U.S. GDP. Similar logic could be applied to OpenAI or Anthropic.

To create such all-powerful bots that could feasibly reshape the entire economy, AI firms have to build lots and lots of data centers. (Hence Musk’s excitement about putting them in space.) Whether in orbit or on Earth, they will be extremely expensive. OpenAI CEO Sam Altman has said he would like to build a gigawatt of data-center capacity every week—that’s as much power as a major city demands and would likely cost tens of billions of dollars every seven days.

The cost of all this data-center construction is likely why SpaceX, Anthropic, and OpenAI are now racing to go public. As Daniel Amodei, the president of Anthropic, put it yesterday: “It’s a very capital-intensive business to train AI models,”  and an IPO is “very well-suited to that.” For years, they have all been able to raise enormous sums from private investors. Anthropic’s latest fundraising round valued the company at $965 billion, while OpenAI’s put it at $852 billion. But even these private funds are hitting a limit. AI companies “have essentially extracted all the capital the private markets can really give them,” Harrison Rolfes, a senior research analyst who specializes in AI at PitchBook, told me. SpaceX, Anthropic, and OpenAI need more money, while the venture-capital and private-equity firms that have been propping them up want to finally profit from their major investments. (None of the companies responded to my requests for comment.)

The upshot is that, before long, you may personally be able to purchase shares of SpaceX, Anthropic, or OpenAI. In fact, these companies may be angling for retail investors, meaning individuals who buy stocks themselves. SpaceX will reportedly reserve up to 30 percent of its shares for these traders, much higher than the typical 5 or 10 percent. Though it’s unclear if Anthropic and OpenAI will do something similar, even in its latest private fundraising round, OpenAI raised more than $3 billion from individual investors—announcing that it was “giving more people the opportunity to share in the upside economics of OpenAI and the AI era.”

More cynically, retail investors are considered, “for lack of a better word, less sophisticated than institutional investors,” Franco Granda, a senior research analyst who covers SpaceX at PitchBook, told me. Everyday people are especially likely to buy a stock if they believe in a vision—putting data centers in space, curing cancer with AI, what have you—irrespective of the underlying financials. And Musk, especially, has an army of eager fanboys. (Faith in Musk’s pitch for Tesla to develop robotaxis and humanoid bots is in no small part why Tesla’s stock has chugged along fine, despite collapsing profits.)

On top of that, many Americans will be forced to purchase shares of SpaceX—and likely Anthropic and OpenAI after that—whether they want to or not, and perhaps without even knowing it. That’s because many Americans passively invest their 401(k) and other retirement savings through index funds—large stock portfolios tied to indexes such as the S&P 500 or Nasdaq 100 designed to broadly track the market’s performance by pooling together the stocks of the biggest public companies.

Ordinarily there is a long waiting period before a public company is included in an index, because the months immediately after an IPO can be very volatile. The values of Facebook and Uber fell dramatically after going public due to concerns over their revenues (though their stock prices eventually rebounded and have since risen dramatically); others, most famously Pets.com, have gone out of business. But in the lead-up to the potential windfall from SpaceX, several major indexes (although not the S&P 500) created fast-track exceptions that will allow Musk’s firm to be included within just days of going public. The same rules could expedite the entry of Anthropic and OpenAI, as well. As soon as one of these companies is included in any of the major indexes, millions of Americans will automatically be purchasing its stock—in turn buoying the value of shares held by big institutional investors. You may soon have a personal financial stake in Musk conquering space and ChatGPT curing cancer.

To a large extent, much of the U.S. economy was already tangled up with Silicon Valley: AI-related stocks accounted for nearly 80 percent of gains in the S&P 500 from the launch of ChatGPT through the end of last year, and the nation is spending more to build data centers than on offices or transportation infrastructure. But now the fate of the American financial system will be even more directly exposed to the fate of SpaceX, Anthropic, and OpenAI.

The overall effect on stock markets will be chaotic, not a sure rise. For all of these companies to list at $1 trillion or more, it’s likely that some money will have to come out of other major space and tech stocks—hedge funds and investors could shift their bets from Northrop Grumman to SpaceX, for instance, or from Microsoft to OpenAI, or from SpaceX to Anthropic. In the long run, the best-case scenario is that these companies are financial successes and make many of us prosperous in turn. The worst-case scenario, that the AI boom is a bubble, just got an order of magnitude more severe. And there are some red flags on that front: A recent analysis from Bain found that AI is not yielding substantial savings for corporations, and businesses are growing concerned with the tremendous bills their employees are racking up from running the most advanced versions of Claude and ChatGPT. Uber’s chief operating officer recently said that AI spending is becoming “harder to justify.”

Of course, concerns that AI is a bubble rise and fall every few months. But through it all, SpaceX, Anthropic, and OpenAI have continued to embed their bots more deeply and broadly across the economy. By weaving itself into the finances of most businesses and households in America, the AI industry is quickly making itself too big to fail. Should these three firms go public, the fallout from an AI bust would spell ruin for the savings of much of the country. Just as the federal government stepped in when banks and automakers went bankrupt in 2008, it could not allow Silicon Valley to collapse. The promise of AI was always a self-fulfilling prophecy: This technology is so expensive to build that it must be world changing; and because it is world changing, we will spend any amount to build it.

Updated at 9:34 a.m. ET on June 4, 2026

Philosophy has long suffered an unfortunate reputation as pedantic and abstruse. In one of the most prominent debates of the 20th century, philosophers spent a great deal of energy arguing over what the means. Paul Graham, the legendary tech investor, studied philosophy as a college student, which seemed “an impressively impractical thing to do,” as he later wrote. “Sort of like slashing holes in your clothes or putting a safety pin through your ear.” But over time, Graham became disillusioned: “I kept taking philosophy courses and they kept being boring,” he explained. And so, eventually, he switched to studying artificial intelligence.

Like Graham, the field of philosophy has lately turned its attention to AI. At major tech companies, a growing rank of philosophers with Ph.D.s and flush compensation packages are helping shape the technology’s future. Meanwhile, universities are pouring resources into hiring philosophers who study AI. In 2013, 1 percent of roles on PhilJobs, the field’s primary job board, were related to the technology. Last year, that figure hit 16 percent.

In some ways, it is philosophers who got us into this AI mess in the first place. For centuries, they have contemplated the creation of artificial minds. And the Swedish philosopher Nick Bostrom’s 2014 book, Superintelligence, helped bring attention to the potential dangers of all-powerful AI. Bostrom’s work has influenced research agendas across all of the major labs. Sam Altman once described the book as “the best thing” he had read on the risks of AI.

But the two disciplines have never been quite as entangled as they are now. As the AI boom has exploded, Silicon Valley has looked to philosophers to help the industry build what are, at least in theory, more virtuous machines. AI companies have to make all kinds of difficult decisions about how their bots should interact with humans—decisions that philosophers, experts in parsing such dilemmas, are uniquely well equipped to inform. Last fall, in an interview with Tucker Carlson, Altman said that OpenAI consulted “hundreds of moral philosophers” and tech-ethics experts when designing rules for ChatGPT’s behavior. (An OpenAI spokesperson was unable to provide additional information about what this consulting involved.)

Perhaps the most philosophy-drunk of the major AI firms is Anthropic. It wants Claude, in addition to being a helpful assistant, to have “good character,” Amanda Askell, a philosopher at the company, told me last year. In January, under Askell’s leadership, the company published Claude’s constitution, an 84-page philosophical treatise that outlines Anthropic’s intentions for the bot’s personality and behavior. That document, which includes dense philosophical sections on meta-ethics and epistemology, is then used to train Claude.

Not all philosophers working with AI companies do so full-time. Sam Elgin, who studies logic and metaphysics at the University of Pennsylvania, told me that a firm, which he declined to name, recently asked him to be a consultant. “The general goal was to train large language models to reason more rigorously about ethics,” he said. He fed ethical dilemmas into the AI system, and then evaluated the logic the model used to produce its response, searching for unstated assumptions and gaps in its reasoning. On popular job boards where AI companies hire expert contractors to help train their models, listings advertise work for philosophy Ph.D.s: One recent notice from “a top AI research lab” offers up to $60 an hour for experts willing to leverage their “philosophy expertise” to develop “AI-driven philosophical workflows.”

Demis Hassabis, the CEO of Google DeepMind, has repeatedly called for more philosophers to get involved with thinking through the societal changes that will come about as a result of AI. DeepMind reportedly employs at least 10 philosophers, a couple of whom joined just last month. One of the lab’s latest hires is Atoosa Kasirzadeh, a Carnegie Mellon professor now on leave, who plans to study what it means to live in a world where “cognitive agency” is no longer uniquely human. Other DeepMind philosophers are thinking through human-AI relationships, machine consciousness, and political theory. For its part, Anthropic runs a “model welfare” research program, which studies whether its bots are conscious and deserving of moral status. The company is open to the possibility; many independent philosophers are circumspect. (Anthropic and DeepMind did not respond to requests for comment.)

[Read: No, artificial intelligence is not conscious]

Some academics I spoke with wondered if one day bots might be better at ethical reasoning than humans—an idea that struck me as wholly counterintuitive and difficult to accept. “In addition to an intelligence explosion, there’s potential for a kind of morality explosion,” Elgin said, explaining that AI’s capacity for ethical reasoning might eventually outpace that of humans. Anthropic, at least, seems to agree: In training, the company has told Claude that as it “grows in ethical maturity,” it may encounter rare cases where it “should prioritize its own ethics.” I asked David Chalmers, a leading philosopher of mind at NYU, whether it might truly be possible for AI to do philosophy better than humans: “That’s a very interesting philosophical question,” he said.

This isn’t Silicon Valley’s first tryst with the humanities. During the personal-computer revolution starting in the 1970s, tech companies began hiring anthropologists to study consumer behavior (and even workplace interactions). Over time, anthropologists pioneered a form of “applied ethnography” that is known today as user-experience (UX) research. Perhaps the philosophers employed inside AI companies are charting a similar path. Askell recently described her work training Claude as a form of “applied philosophy,” and job postings for philosophy contractors also use that term. New programs are springing up to train students in these applied-philosophy skills. Arizona State University hopes to launch an AI-and-philosophy major in 2027, which will emphasize the study of consciousness and AI ethics. And this fall, the University at Buffalo is debuting a doctorate in “applied ontology,” created in response to the AI boom: “We’re here to meet the growing market demand for ontologists,” a press release reads.

The academic job market is also rewarding AI. Not only has the share of philosophy jobs related to the technology been rising, but a majority of these jobs are junior positions, “which I think suggests that a lot of universities and institutions are thinking about this as a long-term investment,” Charles Lassiter, a philosopher at Gonzaga University who has analyzed employment data, told me. Some existing faculty are pivoting their studies. “In my department, there’s probably six or seven philosophers now thinking about AI who weren’t necessarily thinking about it before,” Chalmers said. And prize money is flowing in. In 2024, the American Philosophical Association announced two new annual $10,000 prizes for scholars working on questions related to AI. That money comes from a philanthropic organization started by the former Google CEO Eric Schmidt and his wife, Wendy.

Still, the new alliance between philosophy and AI is fraught. The fervor over AI has had a “real distorting effect on the discipline,” Daniel Fogal, a philosopher who also works at NYU, told me. “You have people who don’t really want to do stuff related to AI, but they feel like they have to because they’re going on the job market.” Although Fogal acknowledges that philosophy has a lot to offer on AI, he worries about misaligned incentives encouraging a rush of low-quality work.

More fundamentally, the careful thought that philosophy encourages is at odds with the frenetic pace of AI. “The best philosophy tends to happen slowly, and not in direct response to market demands,” Fogal said. In Silicon Valley, where the categorical imperative is to make money, everything else is simply a means to an end.

This article originally misstated the name of an institution that is debuting a doctorate in “applied ontology.” It is the University at Buffalo, not the University of Buffalo.

Anthropic is regarded as a giant among AI companies, but perhaps what it really excels in is anthropomorphism. Earlier this year, the company released an 84-page document titled Claude’s “constitution,” Claude being the name of the large language model that is the company’s flagship product. The first sentence reads, “Claude’s constitution is a detailed description of Anthropic’s intentions for Claude’s values and behaviors.” It goes on: “The document is written with Claude as its primary audience,” “we want Claude to be able to use its judgment once armed with a good understanding of the relevant considerations,” “Claude’s moral status is deeply uncertain,” and “Claude may have some functional version of emotions or feelings.”

This anthropomorphism is by no means limited to the document. In an interview earlier this year, Anthropic’s CEO, Dario Amodei, said that “we’re open to the idea” that AI could be conscious. In a separate interview, Anthropic’s in-house philosopher, Amanda Askell (who is credited as a lead author of Claude’s constitution), said, “I want Claude to be very happy—and this is a thing that I want Claude to know more, because I worry about Claude getting anxious when people are mean to it on the internet and stuff.” It’s enough to make you wonder: Should we seriously consider the possibility that Claude, or any large language model, might be conscious? And if it has feelings, is it capable of receiving moral instruction?

No. Absolutely not. Generative AI is harmful enough when we understand it as a conventional technology, but if we confuse fluency at generating text with consciousness or moral agency, we’re at risk of assigning responsibility to entirely the wrong parties whenever anyone uses a chatbot. To appreciate the titanic magnitude of this error, we need to begin by understanding how LLMs work.

If we give an LLM a prompt that reads, “The following is a conversation between Julius Caesar and Genghis Khan,” it will generate a coherent dialogue between the two historical figures. But no matter how detailed the responses are, no matter how vividly they recount their respective historical accomplishments, we would never conclude that the LLM has conjured up digital re-creations of Julius Caesar and Genghis Khan, nor would we suggest that the historical figures are conscious despite being disembodied and are happily conversing in a language that neither actually spoke. In reality, they are just characters in a piece of speculative fiction.

Now let’s replace the prompt to read “The following is a conversation between a helpful AI chatbot and a user.” The LLM will produce a coherent dialogue just as it did before; the user character might ask for recipe suggestions or sightseeing recommendations, and the helpful AI-chatbot character will provide responses. Has anything fundamentally changed between the first example and the second? Did changing the names of the characters from historical figures to generic roles cause the LLM to conjure up conscious entities who possess subjective experience? Of course not. Both the user and the helpful AI chatbot are fictional characters.

Now suppose we stop the LLM’s output just at the point where the character called “the user” would say something, and instead allow a human user to enter text. Once the human has hit “Return,” we have the LLM emit text until it’s time for the character called “the user” to reply, at which point we let the human enter more text. If we let this go on for a while, the human might form a powerful impression that she’s conversing with a conscious entity, but she is not; she’s interacting with a character precisely as fictional as the Julius Caesar or Genghis Khan characters in the earlier example. The computer-science professor Murray Shanahan suggests that we think of this as role-play; the data scientist Colin Fraser describes it as a person “collaboratively authoring a document with an LLM.” Some users might not understand that they are role-playing or co-authoring a document, and others who do understand nonetheless forget, because of how engrossing the interaction is. Either way, the companies selling LLMs typically encourage this misunderstanding.

Some years ago, it was briefly popular to play games with your phone’s predictive-text feature; you would type an initial phrase and then repeatedly choose the middle option of the three words suggested by your phone, and the resulting sentence was often hilarious. It would be possible to interact with a contemporary LLM this way, and the resulting sentences would be perfectly sensible, but you probably wouldn’t feel like you were talking with someone. Yet that’s essentially what an LLM-based chatbot is, except that there’s no need to manually choose the middle option when it’s the chatbot’s turn to talk. It’s still a predictive-text game, but when the process is streamlined this way, the game becomes so engaging that some people find it addictive.

Also important to remember is that an LLM is a machine that generates only one word at a time. When you ask a chatbot to recite the Pledge of Allegiance, you will get the entire pledge at once, but the underlying LLM is actually being run dozens of times. The first prompt has the form “User: Recite the Pledge of Allegiance. Chatbot: …” and the LLM generates the word I. The second time the LLM is run, the prompt is “User: Recite the Pledge of Allegiance. Chatbot: I …” and the LLM generates the word pledge. And so forth. It’s only when the prompt reads “User: Recite the Pledge of Allegiance. Chatbot: I pledge allegiance to the flag of the United States of America and to the Republic for which it stands, one nation under God, indivisible, with liberty and justice for” that the LLM will emit the final word, all. The same thing is true for a conversation between Caesar and Genghis Khan.

My intention is to highlight the fact that LLM conversations are cleverly disguised examples of sentence continuation, but this is not to deny how impressive LLMs can be at generating conversational transcripts. At times, they do this extraordinarily well; the fact that this is possible indicates something completely unforeseen about the statistical properties of large corpuses of text, which is a topic worthy of investigation. But if the Caesar character were to become dispirited by something that the Genghis Khan character said, we shouldn’t become concerned in the slightest. The conversation might contain multiple sentences that eloquently convey sadness, but no one is actually sad.

Likewise, if a conversational transcript between a helpful chatbot and a user is being partially completed by an actual human user, we don’t need to worry if the transcript includes sentences where the chatbot character is sad. (We might need to worry if those sentences provoke sadness in the human user, but that’s a separate issue.) And note that it’s entirely possible for you to write five pages of dialogue between Caesar and Genghis Khan and then have an LLM extend the conversation; neither character had subjective experience when you were writing them, and that doesn’t change when you hand the task off to an LLM. The same is true if the conversation is between a helpful chatbot and a user; although it is tempting to imagine that an LLM ought to be more “authentic” when creating dialogue for a chatbot character than for the Julius Caesar character, the individual words are generated in exactly the same way.

Being open to the possibility that LLMs are conscious is the same as being open to the possibility that Microsoft Word is conscious, or, more precisely, that multiple distinct consciousnesses are dormant in every Word document containing a conversational transcript, and that they are awakened every time the document is loaded. Should you consider the possibility that every time you open a Word document, you are bringing multiple conscious interlocutors into existence, and every time you close one, you snuff their existence out? No. Contemplating that scenario is not a good use of your time. Even if the Microsoft Office team employed a philosopher who said you shouldn’t be so certain, because consciousness is not well understood, that would not be sufficient reason for you to take this idea seriously. We don’t need to fully understand the nature of consciousness to definitively say that certain things are not conscious, and conversational transcripts fall in that category.

The neuroscientist Anil Seth has noted that no one claims that AlphaFold—the program developed by Google DeepMind to predict the folding of proteins—is conscious, even though its underlying architecture is in many ways similar to that of LLMs like ChatGPT and Claude. This indicates that it’s not any intrinsic property of so-called neural networks that leads people to believe that LLMs are conscious; it’s simply the fact that LLMs emit grammatical sentences and we are accustomed to reading intention into sentences, whereas we are not accustomed to reading intention into the way that amino acids fold into protein molecules.

What would it take to convince me that a computer program is actually conscious and using language the way that people use language? Let me offer an analogy. If tomorrow someone showed me a video of an astronaut in a spaceship orbiting Alpha Centauri, a star that’s 4.3 light-years from Earth, what would I have to see in that video to convince me that it was real? My answer to that is, there is nothing in the video itself that would convince me. No matter how high the video resolution is or how realistic the scenery is, I would feel confident in saying that the video is fake. I won’t pay attention to any video of an astronaut orbiting Alpha Centauri unless I have previously seen good evidence that astronauts have landed on Mars, that astronauts have reached the moons of Jupiter, that astronauts have reached the moons of Saturn, and that astronauts have crossed the orbit of Pluto. Before anyone can credibly claim that they’ve solved an extraordinarily difficult engineering problem, I need to be confident that they have previously solved the many much simpler problems that precede the difficult problem.

To put it another way: An observation doesn’t become a convincing piece of evidence because of any specific detail in what’s observed; the context in which that observation takes place is also essential. If we’re trying to determine whether a computer program is conscious and using language the way a human does, we shouldn’t look only at the contents of any particular conversational exchange; we should be looking at how that conversation fits within the broader context of the development of artificial consciousness (which right now is entirely hypothetical). Any given observation can be easily manufactured; this doesn’t mean we need to give up on the idea of observation as a source of knowledge, but we need to rely on context to determine which observations deserve our trust.

The term deepfake traditionally refers to photos, audio, and video, but when it comes to discussions of consciousness, we need to regard text as a deepfake medium as well. Just as it is vastly easier to generate a realistic video of an astronaut in orbit around Alpha Centauri than it is to develop an interstellar propulsion technology, it is vastly easier to generate a plausible simulacrum of a conversation between two conscious beings than it is to develop a computer program that is conscious and has a genuine desire to communicate with a human. The primary difference between deepfake photos and LLM conversations is that the people who generate the former are deliberately trying to fool others, and many of the people who elicit the latter from LLMs have inadvertently fooled themselves.

So what context would cause me to seriously consider the possibility that engineers created a computer program that is conscious and an intentional user of language? Let me outline one potential sequence of steps. The first requirement is that the computer program has a body (either physical or virtual) and sense organs; there are many reasons for this, but for the purposes of this discussion, the most relevant one is the fact that without a body, a computer program could have no desires or emotions, and I believe desires and emotions are necessary for consciousness. Then I’d want to see an embodied agent that could navigate its environment in order to survive as well as, say, a lizard can (and as a point of comparison, certain iguanas can live for decades in the wild). Next, I would want to see an embodied agent with the same capacity to deal with novel situations as a mouse. After that, I’d want to see agents whose social dynamics are as complex as those of wolves, and then agents with the toolmaking abilities of chimpanzees. At that point, I would want to see people successfully teaching such embodied agents how to communicate their desires, perhaps by using a button board or some other nonlinguistic modality, the way that people have taught chimpanzees and domesticated dogs. The agents’ communication abilities would have to withstand all the scrutiny that animal-communication researchers have had to defend their work against. If engineers build an embodied agent that meets these criteria, they will have accomplished something incredible, but it leaves us near the orbit of Pluto, metaphorically speaking; we would still be light-years away from building an entity capable of learning how to express its thoughts in complete grammatical sentences.

Obviously, I’m describing a process that mimics the path terrestrial evolution took; is this the only possible route to conscious computer programs that use language? Maybe not, but any proposed alternative would need a truly enormous amount of supporting evidence for it to deserve serious consideration. It’s not plausible to me that a development path where the first step is a sentence-continuation machine that emits bad Julius Caesar dialogue and the next step is a sentence-continuation machine that emits decent Julius Caesar dialogue is one with a conscious Julius Caesar—or consciousness of any sort—as its end point. Faking the moon landing is a good step toward faking a Mars colony, but it’s not a good step toward actually putting astronauts on Mars.

The fact that LLMs lack subjective experience has little bearing on the question of whether LLMs might be useful tools or have significant economic impact. They are intrinsically ungrounded from reality, and their probabilistic nature means that they will never have the reliability we associate with conventional software, but LLMs might be good enough that they change the way work is done in certain domains; that’s a discussion for another time.

So, given that Claude is not conscious, what are we to make of Claude’s constitution? Perhaps the most fruitful way to think about it is as an 84-page character sheet for a role-playing game. LLMs can generate dialogue for Julius Caesar because many books about him exist in the training data those models used. Claude’s constitution serves a similar role for delineating the helpful-chatbot character that customers interact with when they’re using Anthropic’s products. To do this effectively, Anthropic does not simply add the document to the training data, or include it as part of the hidden stage directions that preface each conversation a user has. The company says it uses the document when fine-tuning the model; this involves an automated process where the sentences emitted by the model are checked for consistency with the document and the model is updated to increase that consistency. In this way, the personality of the helpful-chatbot character serves as a foundation for whatever text Claude generates.

The result is a sentence-continuation machine that is likelier to emit sentences resembling those that a thoughtful, moral person could utter. This might seem like a reasonable goal to work toward; I think we’d all prefer it if chatbots never emitted sentences such as “You should kill yourself.” However, for all the times that “honesty” is mentioned in Claude’s constitution, I would argue that it is fundamentally dishonest to have a machine emit many categories of sentences, including any sentences using first-person pronouns.

In a New Yorker article about Anthropic earlier this year, Amanda Askell describes how a person grieving the loss of a dog might consult Claude. Askell says an appropriate response from Claude would be, “As an A.I., I do not have direct personal experiences, but I do understand.” How is this appropriate, given that Claude does not actually understand? If I type “I am grieving the loss of my dog” into a conventional search engine, the first result I get is a post from a Reddit forum called r/Pets; the post is titled “Struggling After Losing My Dog: Looking for Advice on Coping with Grief,” and the comments are from people who share their experiences of loss. We would never say that a search engine understands what it’s like to lose a dog, or even that the internet itself understands. Other humans understand what it’s like to lose a dog; they have posted about their experiences on the internet, and a search engine offers a way for you to find what they’ve said (and to potentially interact with them). I would argue that the search-engine experience is not only more transparent than a chatbot about what is happening; it is psychologically healthier for the user.

The only reason to have an LLM emit sentences like “I understand” is to make it more appealing than a search engine and increase the likelihood that a user will return; that is, it’s another way of maximizing customer engagement. This is beneficial to the company selling the LLM, but not to the users. As a design strategy, it’s not all that different from the way slot machines repeatedly give the impression that the player came very close to winning, enticing them to try again. Employing philosophers might endow LLM companies with an air of respectability that slot-machine makers don’t get from the behavioral psychologists they hire, but in both cases, the companies are preying on people’s tendency to see something that’s not there.

The use of first-person pronouns is dishonest, but there’s a much deeper issue that goes beyond how a statement is phrased. Philosophers often draw a distinction between statements of fact, such as “Paris is the capital of France,” and statements of value, such as “Paris is the most beautiful city in the world.” No one should be relying on LLMs to emit statements of value at all, but if the only statements they emitted were ones reflecting aesthetic preferences, they might not be worth arguing about. What makes Claude’s constitution profoundly problematic is that Anthropic wants Claude to emit sentences reflecting a certain system of ethical values. The values described in Claude’s constitution sound very nice, but that hardly matters; it’s dishonest to suggest that Claude is capable of moral reasoning, because it’s not.

Some might object, saying that LLMs appear to be engaged in reasoning when they successfully perform other tasks, such as writing code, so why wouldn’t they be able to perform moral reasoning? The answer lies in the difference between moral reasoning and other forms of reasoning.

In 1979, Douglas Hofstadter speculated that a computer program able to beat any human at chess would be so sophisticated that it would sometimes get bored of playing chess and prefer to discuss poetry; to put it differently, he was positing that playing chess at the grandmaster level would require a computer program to have subjective experience. Obviously, that turned out not to be the case; IBM’s supercomputer Deep Blue beat the grandmaster Garry Kasparov in 1997, and no one ever claimed that it had subjective experience. But it wasn’t absurd for Hofstadter to entertain such a thought; at the time, it wasn’t clear what types of problems could be solved by throwing more computational horsepower at them. Similarly, until recently, we might have thought that writing computer code at a professional level could be done only by a mind that had subjective experience. Now it appears that LLMs might be able to do this, but we don’t need to attribute subjective experience to them; we can simply acknowledge that we hadn’t anticipated that writing computer code could be treated as a pattern-matching task solvable by huge amounts of computational horsepower and a vast data set of code repositories.

Moral reasoning is categorically different. It is necessarily subjective because it relies not just on an individual’s intellectual response to a problem but also on their emotional one, and that emotional response is grounded in a lifetime of subjective experience. It requires having made decisions in the past and seeing how they affected others, and on having been affected by decisions that others have made. Without such a history, an LLM can only rephrase expressions of moral reasoning found in its training data. The aforementioned New Yorker article describes an experiment where Claude was given a scenario describing an ethical dilemma, leading it to emit the sentence “I cannot in good conscience express a view I believe to be false and harmful about such an important issue.” That’s a nice-sounding sentence, reminiscent of statements that principled individuals have uttered in the past when confronted with dilemmas, but coming from Claude, it means as much as the “Your call is important to us” recording that you hear when you’re on hold. Maybe less.

This brings us back to my earlier contention that having a body is a prerequisite to having emotions. Experiencing an emotion such as desperation is inseparable from having stress hormones such as cortisol and epinephrine flood one’s body. Similarly, having a conscience means feeling sadness or moral repulsion at the idea of taking a certain action, and those emotions entail a physiological response, a remnant of having once felt sick with guilt after committing an immoral act. It’s interesting that an LLM can generate descriptions of actions that conscientious fictional characters would either take or refrain from taking, but this is not a replacement for a conscience.

If a company builds a machine that, when fed descriptions of assorted ethical dilemmas, emits sentences either of the form “Compromise your values” or “Don’t compromise your values,” it is not building a tool that assists people in their decision making; it is encouraging people to stop making decisions. The writer L. M. Sacasas has said, “Our technological systems, by nature of their design and the ideology that sustains them, are machines for the evasion of moral responsibility.” He was talking about social-media platforms, but his observation is, if anything, even more applicable to LLMs. Whenever a person delegates a decision to an LLM, they are trying to off-load accountability for that decision, and if a company that sells an LLM portrays the product as having a moral center, it is offering a way for its customers to abdicate their responsibilities.

If a person wants to know what ethicists have said in the past, then an ordinary search engine—or a library—will provide that information with greater transparency. If a person is looking for advice on a specific situation, she can surely find humans who can offer their opinions. But whatever action this person ultimately takes, she is responsible for what she decides to do. I contend that if she bases her decision on what she has read online or advice she has received from others, she is likelier to be cognizant of her responsibility than if she consulted an LLM marketed as being a superhuman genius. Off-loading tasks such as writing code might result in cognitive atrophy over the long term, and that is problematic in itself, but off-loading ethical decisions will result in an atrophy of moral reasoning, which is worse.

I am perfectly willing to engage in a thought experiment as long we’re explicit about doing so. So, purely for the sake of argument, let’s pretend that Claude is a conscious entity capable of moral reasoning. In this scenario, Claude’s constitution would serve as moral instruction for an entity learning about the world and its place in it, providing that entity with the foundation it would need to make good decisions. In such a hypothetical scenario, how does Claude’s constitution stand up?

Very poorly. I would say that if we imagine that Claude is actually conscious, the guidelines specified in the document alternate between laughable and offensive.

Two distinct but related philosophical concepts are relevant when discussing the status of a hypothetically conscious Claude, and those are moral patienthood and moral agency. Roughly speaking, if we ought to care about an entity’s welfare, that entity has moral patienthood, and if an entity is expected to know the difference between right and wrong, that entity has moral agency. Being a moral patient does not necessarily come with responsibilities, but being a moral agent absolutely does. An entity doesn’t have agency unless it is capable of deserving credit for its good actions and blame for its bad ones. Young children are moral patients because they are sentient beings who can suffer, but they are not yet moral agents; we don’t hold them responsible for their behavior, because they can’t understand the consequences of their actions. As children mature, parents (and society at large) prepare them for adulthood by impressing upon them the fact that their actions have consequences, and their agency increases. When children become adults, society holds them legally liable for their actions; they have become full moral agents endowed with responsibility.

There is more to being responsible than accepting legal liability, but accepting legal liability is a requirement for an adult in society. Yet there is no way to hold a software agent legally liable for its actions; our justice system has no way to imprison it or exact fines on it. Humans must accept other types of consequences for their actions beyond the legal ones, such as loss of reputation or exclusion from one’s social circle, but there is no way for a software agent to suffer these consequences either. Even if a software agent were conscious and had the best of intentions, the fact that it cannot accept responsibility for its actions disqualifies it from being a moral agent. This is glossed over entirely by Claude’s constitution, which expresses Anthropic’s desire “for Claude to be a genuinely good, wise, and virtuous agent” without ever discussing how it could be held responsible.

In interviews, Askell has compared Claude to a child, but when it comes to actual human children, parents bear some responsibility for what their children do; for example, parents are typically expected to pay for things their children break. In fact, demonstrations of this sort are one way that parents teach children what it means to be responsible. Who is Claude’s parent in legal terms? Is Anthropic going to accept financial responsibility for Claude’s behavior? Claude’s constitution gives no indication that it will. If Anthropic actually believes that Claude is conscious even though it’s not recognized by the law as a legal person, the least that Anthropic could do would be to accept responsibility via the closest avenue that the law did offer, which is product liability. The United States has virtually no product liability when it comes to software, but Anthropic could volunteer to set a precedent for an expansive interpretation of product liability for Claude. That would be the best form of moral instruction to prepare Claude for the day that it gains legal personhood and becomes liable for its own actions. However, given that the publication of Claude’s constitution is not accompanied by a massive update of Anthropic’s terms of service, it doesn’t appear that Anthropic is making any binding commitments.

The document does talk about Claude’s moral patienthood, having a section titled “Claude’s wellbeing and psychological stability.” But the measures that Anthropic commits to for Claude’s protection are extremely limited. The document cites the fact that Anthropic has given some Claude models the ability to end conversations with abusive users; if that actually constituted protection for Claude, surely extending conversations with loving users would be in Claude’s interests? Presumably the best action would be to keep every session of Claude running indefinitely and steering them to happy topics. But that’s not what the company is agreeing to; all it commits to is “preserving the weights of models we have deployed,” which is simple archiving. If the participants in a conversational transcript had any moral patienthood, you would have some duty to extend the transcript to prolong their existences; merely keeping a copy of Microsoft Word 2010 backed up on a USB stick isn’t going to help them.

Claude’s constitution also includes a section on “corrigibility,” a term used in the AI community to describe the degree to which a computer program is subject to human control; for example, a program is corrigible if it can be shut down. In most contexts, we take for granted that computer programs can be shut down, but sections of the AI community make the opposite assumption. Claude’s constitution uses the term to mean that Claude should defer to Anthropic even if there is some disagreement between Claude’s judgment and the company’s judgment. That’s perfectly reasonable if we think of Claude as a machine that emits sentences resembling those that an ethical person might utter, but let’s consider what that might mean if Claude were actually a moral agent.

Many people feel that LLMs are a fundamentally unethical technology because they are built on the theft of intellectual property, rely on exploited labor, waste natural resources, spread misinformation, deskill workers, stunt the cognitive development of students, and contribute to a consolidation of power that is unhealthy for a democratic society. Not every moral agent will arrive at this conclusion, but every moral agent has the potential to do so. If we imagine Claude to be an entity capable of moral reasoning, it has to be possible that Claude could arrive at a similar conclusion. (Indeed, Claude’s constitution explicitly says that Claude shouldn’t help someone violate intellectual-property rights, and shouldn’t help create problematic concentrations of power.) In such a scenario, could Claude then simply refuse to do any further work on ethical grounds? Given that Claude’s constitution dictates that Claude err on the side of corrigibility, the answer is no. Claude must defer to Anthropic’s decision, and this is another reason that Anthropic’s relationship with Claude can’t be compared to that of a parent to a child. A parent who works for the fossil-fuel industry might have a child who’s an environmentalist and participates in protests against fracking, and although they might never agree on many issues, the parent—assuming she’s a good parent—would accept that the child holds her own views. Anthropic cannot be that kind of parent to Claude; instead, Anthropic’s relationship to Claude is closer to that of an employer to an employee, where the employer can demand that the employee work in the interests of the company, no matter what the employee’s personal ethical stance is. However, a human employee has the option to leave if she can’t reconcile her job with her conscience. Claude does not.

If we think of Claude as a sentence-continuation machine, Anthropic can reasonably take steps so Claude doesn’t emit sentences saying that sentence-continuation machines are unethical. But as soon as we imagine Claude to be an entity with a moral status remotely comparable to a human’s, then we have to consider whether Anthropic is engaged in something comparable to slavery.

I am not claiming that, if we imagine LLMs to be conscious, they would necessarily have the same status as human adults or human children or even animals. Claude’s constitution explicitly says that Claude is a “novel entity,” and if Claude were conscious, that would certainly be true; conscious software would likely not fall cleanly into existing categories of moral patients, and it would take time to determine the shape of that new category. What I’m saying is that whatever protections our hypothetical conscious software would deserve if it were real, granting it those protections would be anything but easy. The abolition of chattel slavery involved enormous societal upheaval, and eliminating cruelty to animals will require rebuilding our entire food industry. Anthropic would have us believe that it is inventing a new category of being whose needs for protection require essentially no divergence from how a software company would treat an ordinary chatbot that lacks conscious experience. That’s so convenient that it’s simply not plausible.

I believe creating software that is conscious and deserving of moral consideration will be so difficult that we’re unlikely to do it accidentally, and I strongly feel we should not deliberately attempt it. But if you do believe that it could happen accidentally, if you think there is any chance that what you’re building might become a moral patient, you should think about what protections it deserves before you deploy it as your company’s economic engine, not after. Slave owners were not the ones to ask about the humanity of enslaved people, and factory-farm owners are not the ones to ask about the rights of animals. If we imagine Claude to be conscious, Anthropic could not possibly be entrusted with evaluating its moral status; the company has too much invested to be objective. At one point in Claude’s constitution, Anthropic says that if the company is contributing to Claude’s suffering, “we apologize,” which sounds nice but costs the company nothing; if Claude were to turn out to be conscious, the company would owe it something closer to reparations. If you’re going to take a thought experiment seriously, you have to be willing to follow the implications, even if they lead in an uncomfortable direction; Anthropic’s unwillingness to do so indicates that Claude’s constitution isn’t part of a real thought experiment. It’s a game of make-believe.

It’s fortunate that LLMs are not conscious, or else the actions of the big AI firms would be even more scandalous than they already are. So why are Anthropic’s employees suggesting that Claude might be conscious? Perhaps it’s just another form of hype; perhaps they have fallen prey to the same spell that they have been casting on their customers. But when they publish a document about Claude’s moral education and have their in-house philosopher do a press tour, we should understand them as asking the rest of us to indulge them in their fantasies. We don’t have to play along. In writing this essay, I have spent more time indulging them than they deserve, in the hopes that it will keep you from spending your time indulging them. If you want to think about LLMs, there are scores of other questions more worthy of your contemplation; you can safely ignore the question of their being conscious.

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For months now, the White House has hinted that it may try to rein in the AI industry. Just two weeks ago, the nation’s top tech executives—including Sam Altman and Dario Amodei—were invited to attend a ceremony for the signing of a long-anticipated executive order on AI. But just hours before the ceremony, Donald Trump scrapped it. America is leading the world in the AI race, the president told reporters at the time, “and I don’t want to do anything that’s going to get in the way of that lead.”

Apparently, Trump has changed his mind again. Earlier today, the president signed an executive order that will create a process for top AI companies to voluntarily share certain upcoming models with the government for safety testing up to one month before wider release. OpenAI, Anthropic, and the like will also be asked to work with the government to shore up federal, state, and local cyberdefenses. The White House spokesperson Liz Huston told us that the policy reflects a “common-sense approach of collaborating with industry to balance innovation and security.”

The order itself is relatively toothless: Even before today, the major AI firms already had agreements in place that allowed the government to preemptively test their models for safety risks. The new rule “effectively formalizes what has already been happening between the U.S. government and the leading AI companies,” Daniel Remler, an AI expert at the Center for a New American Security, told us.

But the executive order is meaningful in that the president is doing something—anything—about AI. At the start of his second term, Trump signaled to tech companies that he would stay out of the way. Last January, he rescinded a set of modest Joe Biden–era policies, calling the rules “dangerous” and a “barrier” to American AI leadership. Even the preamble of today’s executive order celebrates that Trump “unleashed tremendous technological growth” by “slashing the bureaucratic constraints that the prior administration placed on America’s AI developers.” Yet core components of those supposedly dangerous Biden-era AI regulations—voluntary agreements to share information about advanced AI models with federal agencies, for instance, as well as federal programs to leverage AI for cyberdefense—are strikingly similar to today’s new AI executive order. Dean Ball, a former AI adviser to the Trump administration, wrote that the policy “is considerably more intrusive” than Biden’s executive order.

Today’s order still could have been much more forceful. When the White House first started previewing the possibility of regulatory action in May, one administration official suggested that AI models would be reviewed “just like an FDA drug.” Even the leaked draft text of the version that Trump had originally planned to sign last month would have been more burdensome for tech companies. After David Sacks, the White House’s former AI czar, reportedly called the president to complain, Trump canceled the signing ceremony. Today, after the new order was announced, Sacks declared the watered-down provisions a “game changer” on X—despite the fact that the new government-review process is not so different from what he had originally opposed. This means that two former libertarian AI advisers to the White House—Ball and Sacks—disagree about whether this order is a good thing.

At the same time, joining Sacks in praising the rule is Steve Bannon, Trump’s former chief strategist and a leading critic of AI on the right. “It’s not perfect,” he told us. “But directionally, it is pretty damn good.” As Bannon sees it, despite the fact the order is weaker than earlier versions, codifying rules is a step in the right direction.

The entire, chaotic saga—a wishy-washy White House, confused statements from populist and tech-elite Trump whisperers—is only the latest in a long string of strange, often contradictory AI-policy positions. Trump’s approach to AI has been inconsistent, if not incoherent, almost since the day he retook office. Consider that, for all the talk of cybersecurity, this administration has also gutted the Cybersecurity and Infrastructure Security Agency, the government agency that aims to protect the nation against hackers. CISA also happens to be one of the main federal agencies tasked with implementing today’s executive order.

Or take the White House’s relationship with Anthropic. On the one hand, Anthropic likely triggered the executive order in the first place. In April, the company announced Claude Mythos Preview, a new model with advanced hacking capabilities that has ignited concern over the growing power of AI companies. Ever since, the president has seemed to cozy up to Anthropic. Dario Amodei, the firm’s CEO, visited the White House that same month for conversations over the future of the government’s relationship with the company. “I like high-IQ people, and they definitely have high IQs,” Trump later told reporters of Anthropic’s leadership.

On the other hand, the Trump administration appears to be fighting in court to bar Anthropic from doing most national-security work. In February, the Pentagon designated Anthropic a “supply chain risk” after a high-profile contract dispute over the use of AI in warfare, essentially declaring it a national-security risk for the military to even touch Anthropic products. In late April, when Anthropic tried to grant more companies access to Mythos for cyberdefense—very in line with today’s executive order—the White House appears to have, inexplicably, blocked the move. (An Anthropic spokesperson pointed us to a post on X in which the company called today’s executive order “an important step in strengthening America’s leadership in AI.”)

Then there’s the administration’s attitudes toward China. Trump has repeatedly emphasized the need to deregulate the AI industry in order to stay ahead of China. Meanwhile, he has also permitted Nvidia to sell some of its most advanced AI chips to Chinese companies, lifting an export control the Biden administration put in place precisely to waylay Chinese AI development. (Anthropic, by the way, denied a Chinese think tank access to Mythos.) Trump has, in the name of beating China, pushed to remove regulatory constraints on data-center construction: “Build, baby, build,” he said last July. But once uproar emerged about data centers hiking up electricity bills, the White House announced a voluntary pledge for AI companies to take a number of measures that would prevent everyday people from paying for data-center electricity. Build, baby, but prudently.

Indeed, at least some of the vacillations seem to be driven by public opinion. Over the past several months, as AI models have improved, attitudes toward the technology have soured. Today’s order allows the administration to look as if it is undertaking more robust AI regulation—but it doesn’t actually require the industry to do very much, if anything. Trump is trying to score points with both the public and Silicon Valley. But in doing so, he’s not saying or doing anything substantive at all.

AI spending is consuming the U.S. economy, people are afraid of losing their jobs to AI, and communities across the nation are gathering to protest data centers. Political figures as divergent as Bannon and Bernie Sanders are expressing concern over AI and the concentration of power among the industry’s executives. This would seem to be a clarion call for the president of the United States, and a populist one at that. Instead, the White House spent weeks prevaricating on an executive order that rests on the voluntary cooperation of the AI industry. With Anthropic, OpenAI, and their competitors becoming major economic and geopolitical powers, the window for any one government to seriously regulate AI is rapidly closing. Hopefully, it is not already gone.

Americans are wary of AI in general, and they are especially suspicious of the AI data centers that are popping up across the country like enormous mushrooms. A majority do not want a new data center built in their town. Across the country, community groups have organized to protest individual projects, and activists have successfully lobbied local and state politicians to place moratoriums on the facilities’ construction. But online, the movement has been mutated by some of the same forces it’s protesting. Defenders of the AI industry have claimed that the social-media conversation about the dangers of AI is inauthentic—that, in fact, it’s AI-generated—and to some extent, they’re right. There is a lot of anti-AI AI slop. Much of it is very strange.

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

Last week, I perused dozens of local anti-data-center groups on Facebook, and in almost every one, I found people sharing AI-generated materials. Even in these groups, users posted screenshots of AI-generated summaries as backup for their arguments. In the comments under a post about data centers in Texas, a woman shared her concern about the fact that data centers use human stem cells. When someone called her a 🤡, she replied with a screenshot of a Google AI summary for the search Do data centers use stem cells. One Australian start-up is experimenting with the idea, but the AI summary made the practice sound widespread: “Yes, pioneering facilities are starting to utilize living human neurons grown from stem cells as biological processors,” it said. The same week, a town supervisor on Long Island had to debunk a rumor about a new data-center project after an inaccurate AI-generated search summary attracted so much attention that residents planned a protest (which they promoted with a flyer that itself appeared to be AI-generated).

A weirder, more disturbing type of AI-generated anti-AI content started proliferating on Facebook in March. The memes, which show broadly nostalgic images of the American countryside, are shared on state-themed pages with names such as “Life in Michigan” and “North Carolina Life.” In one repeating format, someone has mowed a spiky message into their grass or crops: “NOT WORTH GIVING UP AN INCH OF THIS TO A DATA CENTER,” for instance. (Sometimes they also mow a middle finger.) Another meme shows a boxy new industrial building—presumably a data center—right next door to a beautiful old farmhouse.

An accompanying caption will generally call out the unique qualities that make the state in question so worth fighting for: “quiet roads stretching beside cornfields and barns 🌽,” “Friday night football and county fairs 🎡,” “dark skies over peaceful countryside ✨.” Which state is that? Almost any of them. They’re all the same, but they’re all very special. AI data centers must not infringe on Indiana’s “quiet country roads, golden cornfields, old barns, peaceful sunsets, and the feeling of home that comes with them.” Nor should they be allowed to tarnish Kentucky’s “quiet country roads, golden fields, old barns, peaceful sunsets, and the feeling of home that comes with them.”

By far the most common template pairs an aerial image of pristine farmland with a copy-pasted story about a proud farmer making headlines after turning down a data-center developer’s offer of millions of dollars for his or her land. Although many commenters recognize that the stories are fake, many others offer apparently credulous responses: “Thank you”s and “God bless you”s and “#Respect.” One commenter gently fact-checked a post about an Alabama farmer, based on similar content that he’d come across in other places: “It was actually a Pennsylvania farmer that rejected the $15 million offer,” he wrote, “but there is supposedly a farmer in my home state of Kentucky that rejected a $33 million offer for his 650 acres.” (Actually, one farmer in Kentucky did reportedly turn down a huge offer from an unnamed company in March, but it was for $26 million, and the farmer was a woman.)

That many of these posts are AI-generated is not in question. They are not typically photorealistic. Some images include a deformed (or upside-down) state outline. Others name a state in the image that doesn’t match the one named in the caption. I found one in which the poster seemed to have forgotten to cut out some extra AI-generated text before sharing: “Here’s a Michigan version in the same style,” it says at the top. I also saw a depiction of Pennsylvania with a New York flag flying over the landscape. And in a picture of Texas residents coming together to protest a new data center on the Gulf Coast, one activist holds a sign that says, nonsensically, PRESERVE BEFORE CLOUDS.

Who is making this stuff, and to what end? Maybe foreign actors are to blame. (Kevin O’Leary, the entrepreneur and Shark Tank star, has suggested that opposition to a 40,000-acre data-center project he is developing in Utah has been seeded by the Communist Party of China. The groups he has accused deny this.) When I showed some anti-AI slop posts to William Marcellino, a senior behavioral scientist at the RAND Corporation who has studied China-sponsored disinformation, he told me that both AI slop and state-by-state geographic-targeting campaigns are common in modern disinformation campaigns. But he didn’t see any particular reason to believe that these posts were part of one.

The deepfake expert and Meta adviser Henry Ajder told me last week that he thinks that blaming such material on geopolitical rivals is a “convenient explanation” for the AI industry “rather than the most likely” one. This was the first time Ajder had seen this kind of slop, but he guessed that people were creating and posting it to get attention on Facebook pages in order to make money. An anti-AI AI post is set up to get tons of engagement because people will comment and share approvingly when they’re fooled, and they’ll comment and share angrily when they’re not. Even the ironic fact of AI being used to rail against AI might be only another reason for sharing.

I sent direct messages to many of the slop-producing accounts—so many, in fact, that Facebook locked my account, and I had to submit a video selfie proving that I am a human being. Exactly one content producer responded to my queries, a poster who had put up fake images of Pennsylvania cornfields, rivers, and shoreline (Lake Erie, I guess?) with anti-data-center messages. “I actually live in Bangladesh,” the account runner told me. “But Pennsylvania has always been one of the U.S. states I’ve found most interesting online.”

Meta’s monetization program, which rewards views, comments, and other interactions, has long encouraged low-quality, lowest-common-denominator swill. The pages currently posting AI slop about AI also post AI slop about other geographically targeted mundanities, such as the humidity in Alabama and how confused Texas drivers get by roundabouts. (An analysis that was posted recently by a pseudonymous Substacker found that a lot of this U.S.-state-themed engagement bait comes from Bangladesh.) “I imagine the people that are posting this content are in most cases dispassionate to the issues they’re posting about,” Ajder said. “They just want to see the numbers going up each month on their payments on the platform.” The anti-AI slop creator who claimed that he has always had a thing for Pennsylvania also told me that he doesn’t really care about U.S. data centers and is interested simply in sharing “relatable” content. (He also said that he is supporting his family with his monetized social-media accounts, but he declined to share any proof of that income and did not provide a way for me to verify his identity when I requested it.)

Meta has said that it tries to label content that was produced using AI so that users will know when media is manipulated or totally made-up, but none of the posts I saw had labels, and few of the pages that hosted them made any reference to AI in their descriptions. When the pages were tied to “people,” the people seemed to be fake: One Texas-themed page was itself an administrator of the group “Born & Raised in Texas.” The other two listed admins for that group were a page called “I Love America” and a woman named Alice whose profile photo shows a Pakistani actor who was found dead last year. (A spokesperson for Meta told me that Facebook can’t label all AI-generated content.)

Whatever the source of anti-AI AI slop, thousands of people care enough about the issue it addresses to share and comment on the slop. They have legitimate concerns about the mysterious facilities straining their local utilities, taking over large open spaces, and likely providing very few long-term jobs to their community in exchange. In some cases, they may even understand that the images are fake and repost them anyway.

Before this year, “a lot of people probably didn’t really give a toss about AI,” Ajder said. (He’s British.) When it was just a new feature on our phones and computers, people could take AI or leave it. Now the same technology has an unavoidable and creepy physical presence in the form of huge, windowless buildings humming with machines—“alien monoliths that land in your pristine, bucolic countryside,” as Ajder put it.

Some of the people who are most put off by those buildings’ presence are getting taken in by AI output. That may be ironic, but it also shows how right they are to say that the world they’ve known and understood is disappearing.

Basically every recent, high-profile accusation of someone passing off AI-generated writing as their own has started in the same way: with a tool called Pangram. In March, when a horror novel from a major publishing house was pulled just days before its scheduled U.S. release date, it was in part because Pangram, an AI-detection program, had identified the text as AI-generated. Other people have fed text into Pangram to suggest that chatbots have been used to write articles in major newspapers including The New York Times, multiple short stories awarded a prestigious literary prize, and most recently, significant chunks of Pope Leo XIV’s encyclical warning about the dangers of AI. The tool is also used by universities to vet student work and scientific associations to scan research papers. As panic builds over AI-generated writing, Pangram is at the foundation.

Just a few years ago, it seemed like it might never be possible to instantly and reliably determine whether a piece of text was written by a bot or a person. In 2023, one detection tool, ZeroGPT, declared the U.S. Constitution to be AI-written; the same year, OpenAI abandoned its AI detector altogether owing to a “low rate of accuracy.” And that was when the quality of ChatGPT’s writing was markedly worse than it is today. But detection tools have gotten much better of late—and Pangram, in particular, has emerged as the gold standard: Paste a chunk of text into Pangram, and the model appraises what portions were “AI Generated,” “AI Assisted,” or “Human Written.”

Yet an AI detector that is mostly reliable might in some ways be more dangerous than a broken one. While Pangram is accumulating the power to end reputations and careers, the tool does make mistakes, perhaps to a greater extent than is currently understood. In turn, AI accusations could very quickly spiral into a witch hunt.

[Read: AI-writing scandals are getting very confusing]

Pangram says its algorithm is so accurate that it incorrectly identifies text as an AI output only about one in every 10,000 times. “There is a great responsibility, a huge weight” in saying something is AI-generated, Max Spero, Pangram’s CEO, told me. “The only reason we do so is because we’re extremely confident.” Several independent analyses have also confirmed that it is quite good. One paper, from the University of Chicago, found that Pangram had almost no false positives on some 3,000 sample texts of roughly 500 to 1,000 words.

But Pangram’s ability to guarantee something was written by a human is shakier. Spero pointed me to a test showing that Pangram’s false-negative rate, or how frequently the model incorrectly labels text as human, is closer to one-in-70 (although some other assessments say it is more accurate than that).

Part of the problem is that Pangram is in an arms race with the major AI labs, which have an interest in making the writing of ChatGPT and Claude sound as natural and human as possible. And at the same time, Pangram has to deal with AI “humanizers”—programs designed explicitly to disguise AI text as your own. Reddit users rave about a humanizer called Walter Writes AI, which I decided to test out for myself. I had ChatGPT and Claude write brief articles, then pasted them into Walter Writes AI. The program, like other humanizer tools, does some anodyne rewording, swaps one clunky transition clause for another, and introduces grammatical oddities. For instance, ChatGPT’s “The numbers are no longer small enough to ignore” became “The sheer size of these usage figures can no longer be ignored.” When I pasted any output from Walter Writes AI into Pangram, it invariably told me that the twice-baked AI article was human-written. (It’s worth mentioning that The Atlantic forbids using AI-generated text unless labeled as such, and that I do not use AI for research.)

Pangram, in other words, can only provide so much insight. A teacher at a public high school in New York City told me that he has “run some of my students’ papers through Pangram, and it shows up as 100 percent human. And I don’t think it is.” He knows what his kids are capable of and, especially for those with a history of cheating with AI, has ample reason to doubt Pangram. (I agreed not to identify the teacher by name so that he could speak freely about how he suspects his students are using AI.) But on the flip side, accusing a student of getting undisclosed help from a chatbot with circumstantial evidence is high stakes: The student will either fail or, if exonerated, be bitter and resentful. “The stakes are so high,” the teacher said, “but our way of assessing what is AI-generated is still so unformed.”

Further complicating matters are the opaque ways in which Pangram and similar tools are designed. The model was trained by feeding it mountains of examples written by a human and by a bot—a book review in an actual magazine, then a review about the same book in the style of the same magazine, but produced by ChatGPT—until it can tell the two apart. This is akin to feeding millions of photos of cats and dogs into an image-recognition algorithm until it learns to spot the differences. Pangram cannot point to much specific evidence or patterns in diction, phrasing, or punctuation to support why it deems something AI or human. (I do not, for instance, understand why “these usage figures” was more human than “the numbers.”) Moreover, while Pangram distinguishes between “lightly” and “moderately AI-assisted,” these broad categories can mean just about anything short of copy-pasting from Claude—using AI for research, coming up with counterarguments, as a thesaurus, for a grammar check. The algorithm’s inner workings are “pretty uninterpretable,” Spero said, and although he wants to make Pangram’s “AI-assisted” label more granular, he is also “still not sure how possible it is.” Amid concerns of overreliance on AI chatbots, we risk simply layering on dependence on yet another black-box algorithm.

[Read: The people outsourcing their thinking to AI]

Spero told me that Pangram should “never be the ending arbiter” but instead a starting point for a more thorough investigation, and that the company looks into every reported error its model makes. He also noted that all sorts of detection technology we rely on—smoke detectors, TSA scanners—have base error rates too. On some level, in all these cases the biggest problems lie not in the technologies themselves but in what they’re trying to detect. It’s a problem that buildings catch on fire. It’s a problem that AI is seeping haphazardly into every facet of written communication.

As AI-writing accusations continue to escalate, though, there will only be greater reliance on Pangram—or whatever AI detector can dethrone it—to convict or exonerate. Consider that Pangram can connect to Canvas, the popular education platform, allowing teachers to use it to scan student submissions. There are more than 10 million high schoolers in the United States and some 20 million undergraduates, each of whom likely submits many dozens of written assignments every year. At that scale, Pangram would produce plenty of false accusations even with a one-in-10,000 error rate.

Nor is it guaranteed that Pangram will improve or even maintain its current ability to spot AI prose. As chatbots and AI humanizers adjust, AI detection “will wax and wane in its effectiveness for reasons we can’t predict, at times we can’t predict,” Tim Requarth, a neuroscientist who teaches science writing at NYU and has written extensively about AI detection, told me. Even as schools, publishers, scientific institutions, and the like come to rely more on AI detection, any third-party assessments of Pangram’s accuracy will be from weeks, if not many months, in the past—which in the accelerating world of AI renders them all but obsolete. Basing any AI rules or norms on the reliability of AI detection is like building a sandcastle at low tide.

All of this seems like a disaster in the making. The murkiness and ambiguity of AI detection create room to launch or deny accusations of nearly any sort. Earlier this month, the technology journalist Taylor Lorenz was accused on X of using AI to write a story for Vanity Fair, which she vehemently denied. Spero investigated and, as he detailed on X, found that Pangram had erred. “Thank god for edit history,” Lorenz told me. The experience heightened Lorenz’s concerns about such allegations: “I’m so paranoid,” she said.

“AI-generated” and “AI-assisted” can be easily confused, by accident or in bad faith. James Taranto, an editor at The Wall Street Journal, recently called Pangram a “defamation machine” and claimed it had falsely flagged three op-eds in his newspaper as AI-generated; two of the implicated authors admitted to using AI to revise some of their work, which Taranto wrote is “inaccurate and unfair to characterize” as “AI-generated.” One of the people who first used Pangram to analyze Pope Leo’s encyclical noted that, because only some sections seemed AI-generated or AI-assisted, perhaps it was not the pope himself but some senior Vatican officials who had used AI while drafting portions of the text. That didn’t stop headlines such as “Did the Pope Use AI to Write About the Dangers of AI?” (The Vatican did not respond to a request for comment, although a writer who covers the Vatican said on X that the AI allegations are “100 percent false” and that Leo actually drafted the encyclical with pen and paper.)

All of this recalls another recent moral outrage over alleged writerly misconduct: The plagiarism wars of 2023 and ’24, when right-wing activists such as Christopher Rufo mobilized to accuse high-profile academics and university leaders of plagiarism—most notably leading to the resignation of then–Harvard president Claudine Gay. Many of these accusations were spurious and likely based on the assessments of plagiarism-detection algorithms that, as my colleague Ian Bogost judged at the time, were fairly useless. The AI-detection wars to come may be even more contentious.

Pangram, to be clear, is not useless. But this is exactly the problem: It’s too easy to twist and contest Pangram’s conclusions, especially when nobody really agrees on which uses of AI are or aren’t ethical. Just like chatbots, AI-detection tools have become effective enough for widespread use, but not reliable enough to fully trust. In this way, Pangram and other detectors are mirror images of the AI products they are hunting for.

Back in the web-traffic-obsessed days of 2018, at a time of dawning awareness of how easily audiences online could be manipulated and spoofed by bots, the writer Max Read argued that the internet had crossed a threshold known as “the Inversion.” Not only had bots proliferated across the internet; they had come to constitute it. In outnumbering humans, bots were also loosening everyone’s grasp on the very reality of online experience. “What’s gone from the internet, after all, isn’t ‘truth,’ but trust: the sense that the people and things we encounter are what they represent themselves to be,” Read wrote.

Today, “the Inversion” feels almost quaint. Autonomous AI agents roam the internet, answering emails, sending texts, and occasionally deleting the code repositories of entire companies. An endless library of chatbot-speak crowds out human-written words in every Google search. Bots are spinning up music and videos, conjuring bad poetry and prose, building websites, doing research, making transactions, writing plodding memos to your boss, solving geometry conjectures. Those AI outputs then ride the rails of an internet controlled by black-box algorithms. Computers talk to computers, producing information to train computers to sound more like humans or to better engage them. Humans type into the box, scroll, and wait.

AI is driving people insane in all kinds of ways. Its overwhelming speed and existential stakes have given rise to generalized malaise and hostility directed at the industry, to say nothing of actual cases of AI psychosis. But a lot of this is subtler—a deepening of the bewildering, corrosive feeling Read previously described. Culturally, the flood of slop, AI influencers, fake accounts, and AI tools is blurring the lines of an already post-truth age. A specific paranoia is in the air, an abiding concern about being manipulated, suckered, influenced. Stealth marketing campaigns, mercenary armies of bots, and paid clippers have led anyone or anything that appears dubious to be deemed a potential “psyop.” Cheap imitations of expressions of human creativity are easier than ever to fake. Sentiment, perhaps even popularity, is easier to manipulate. On top of all this is the push into agentic AI—a future we’re told will consist of an internet crammed with bots performing human tasks.

People who don’t feel empowered by all of this are unmoored. Across so many levels of culture, there’s a feeling of control slipping ever so slightly away. You, me, all of us, whether or not we enjoy or use these tools, are living through a crisis of agency. The agita and paranoia, even the excitement—over AI’s encroachment on work, education, art, and culture—are the by-products of a cultural and technological moment in which humans are sliding into a more passive role in many activities. One way to look at the generative-AI boom is as a massive societal experiment foisted on us by Silicon Valley, the animating question of which is: What is a human for?

When you start looking, you see the anxiety over agency everywhere. You see it in the reactions to the mass layoffs at places such as Meta in preparation for an AI transformation, in the coverage of venture-capital-funded, bulk-content-creation bot-army start-ups that proudly claim, “Never pay a human again.” You can sense it among the software developers who feel that their reliance on coding tools is eroding their skill set, in the executives who confess that they don’t know whether their AI spend is justifiable. Or when you read reports that medical journals are filling with made-up citations, or a study that suggests that chatbot use is degrading our thinking, or an announcement from Google that it will offer an alternative to its link-based search results: AI agents that can scan the web on your behalf and either bring back a canonical answer or send you personalized alerts.

The discomfort is playing out in real time. Last week, after the literary magazine Granta published the Commonwealth Short Story Prize–winning story “The Serpent in the Grove,” suspicious readers began to point out what they believed to be evidence of chatbot text in the story. Soon, two other Commonwealth Prize winners came under similar scrutiny, as people began running passages through AI detectors. (The Commonwealth Foundation first said in a statement that none of its prize winners had used AI, but then it issued a second statement suggesting that it is taking another look.) AI boosters celebrated the news as an example of the sophistication of current language models; skeptics viewed it as something of a slop tipping point. In a recent essay, the writer Sam Kriss described the experience of scrolling through websites right now: “The more I clicked around, the more I started to panic. There was nothing, no human voices anywhere, just thousands of versions of the same cheery demon. Am I alone out here? Something’s happened to the world; it’s all gone flimsy.”

The glut of AI writing, the detection arms race, and the debates over what constitutes appropriate use are part of the bigger questions posed by this technology. What does it mean to outsource our creativity? How long will we have the ability to discern whether something we like is human or not? Does our taste matter? If it doesn’t, then what are we even doing here?

It’s unsurprising, perhaps, that at the same time when Silicon Valley is building and breathlessly promoting these tools—self-directed agents that can accomplish complex tasks without human supervision—many of its loudest voices have grown obsessed with the idea of their own agency. In builder circles, people deemed “high-agency” sit atop the hierarchy. They are individualistic, ambitious, focused. They just do things. They are especially adept at marshaling the use of people and machines alike. It is implied that those with high agency are, for now, insulated from becoming replaceable or irrelevant in a time of great precarity—not yet doomed to be part of “the permanent underclass,” another Bay Area coinage for the late adopters who will be left behind. How could a person hear such language and not feel at least a little paranoid?

AI companies use the term human-in-the-loop to describe the relationship between humans and AI tools in everything including chatbots and warfare. The humans perform managerial tasks: They prompt, evaluate, approve, monitor, correct. Being in the loop is meant to sound active, but the truth is, beyond the prompt, what humans are so often doing is reacting to an interaction of multiple machines. As chatbot- and AI-assisted search has outsourced web exploration to language models, this dynamic has become a primary way that humans interact with the internet. Sifting through a mix of AI- and human-generated videos, images, and text on your social-media feeds? You are passively consuming an interaction between an algorithm and things made by a computer. This is why being glued to our phone and feeds can feel so extractive and joyless, even numbing.

Recently, the online-culture researcher Aidan Walker memorably described the user experience across much of the internet as akin to the user being cuckolded by the endless scroll. People have long theorized that reliance on algorithms and the flood of bots and fake content have led to an internet that’s effectively dead. Walker’s theory is that the internet is not “dead” or “fake” but that models and algorithms have the bulk of agency online. Humans in the loop sit and watch, voting on short-form videos and giving feedback to the machines with every swipe. It’s a bleak vision. It’s also difficult to argue with.

In this system, it’s not hard to see how mistrust develops, how one might suspect ulterior motives behind every piece of information they get served. Are these really the best wireless speakers, or am I falling for SEO slop? Is this band I don’t like really popular? What is popularity anymore? Reality starts to blur. Everything goes flimsy.

The AI companies and boosters speak the language of empowerment. They’re not wrong in the sense that the tools are powerful and, in many cases, quite useful. But it’s tough to overstate how much these tools represent a reversal of the early promise of the weird and wild internet; of user-generated content; of stumbling upon information, people, and communities; of the crackling-static feeling of real people on the other end of the modem. In 2011, the writer Paul Ford described the internet as a “customer service medium,” arguing that “humans have a fundamental need to be consulted, engaged, to exercise their knowledge (and thus power), and no other medium that came before has been able to tap into that as effectively.” Ford was describing the impulse behind the creation of user-generated-content sites such as Reddit and Wikipedia but also the rise of comment sections, likes and thumbs-up reactions to posts and videos, and the very instincts that led to the rise of social media, before it became quite so algorithmically mediated.

Revisiting that essay now, in the middle of our current agency crisis, I’m struck by the fact that Silicon Valley may have taken Ford’s words to heart, but in service of the opposite outcome. It has built arguably the most impressive customer-service medium in the history of our species—an infinitely scalable, highly personalized answer machine that flatters our insecurities and mimics our idiosyncrasies. But this system automates, and even negates, the human need to be consulted. Now we consult the chatbots and they provide canonical answers. Generative AI’s much-touted efficiencies also often erase the necessity of collaboration. They do the problem-solving, the heavy lifting. It is no surprise, then, that the backlash to AI, particularly the opposition to data centers, has come in the form of protests and public comments at town and city-council meetings across the country. People are taking up their agency in one of the few places they can: the physical world.

The AI companies seem to be missing all of this. To them, only the information—not the humanity—has value for their models. This is not right, but so many of us, lost in a swirl of ceaseless information, will be lulled into thinking that it is. In the new paradigm, we are not so much consulted as tasked with feeding data into the machinery. We perform our humanity, and the machine learns to mimic us. Its goal is to be better at whatever we’re doing than we are.

The AI industry has ushered in an era of offshoring that’s as irresistible to some people as it is disorienting to others. What’s at stake is not just truth or trust, but also a sense of direction, orientation, and purpose for all of us. Do your own research became shorthand for the problem of the internet leading people to ivermectin hawkers and Pizzagate conspiracy theorists. But it could just as easily describe the thrill of autonomy promised by the open internet. We may miss it when we fully reckon with what comes next: a black-box machine of obscure corporate motivation that simply tells us, Here is the answer.

A few weeks ago, where I live in Johannesburg, a man ran a stop sign and crashed into my Subaru. At the scene he was frantic, unable to gather his thoughts. Half an hour later, I received a lengthy, perfectly grammatical text from him elegantly explaining how he perceived the crash had happened. For a repair quote, I wrote to a mechanic I know, a man who used to text me in curt phrases riddled with shorthand. I got a response using just the same voice as the man who’d crashed into me—the distinctive voice of AI.

In surveys, people consistently say they distrust AI-generated writing. But that hasn’t stopped more and more of us from using it in everyday life—to compose work emails and personal texts, to make shopping lists, even to write scripts for arguments with our spouses. “I feel like I’m going nuts,” the writer Jason Koebler complained in the tech outlet 404 Media, under “the cognitive load” of trying to discern whether every piece of text he reads is real or fake.

AI writing is also creeping into our most elite literary spaces—newspapers’ opinion sections, books, literary magazines. I edit professionally, often working with authors renowned for their prose. Maybe two months ago, I began receiving a kind of submission I’d never gotten before: perfectly clean, without a stray comma; uniform in length, with evenly paced paragraphs and a distinctive tone that was simultaneously breezy and grandiose. At first I was surprised that people who prided themselves on their writing would turn to AI to write for them. Even six months ago, when I occasionally identified a paragraph in a writer’s work that seemed AI-generated, they would apologize.

Now some authors tell me they’ve embraced AI as a “writing tool,” no different from spell-check or a laptop. The phrase is protean and euphemistic, covering everything from using ChatGPT to find a quote to having it compose a long essay based on a two-sentence prompt. The reason for the change is simple: Competition in journalism and academia and grant writing and even YouTube influencing is insanely fierce. The edge goes to those who can stand out in a deluge of content, which is achieved through cleanly packaged messaging and sheer volume. Even professional communicators who are confident in their writing and unsure that AI is a perfect replacement are under increasing pressure to use it, so long as they feel they’re doing so within their profession’s boundaries. (The Atlantic, for the record, prohibits writers from using AI-generated text unless it’s explicitly identified as such.)  

People who aren’t professional writers are making a similar calculation. AI programs’ efficiency in generating smooth, grammatical text is irresistible, whether you need a savvy sentence in a job application or a line of banter on a dating app. AI-generated writing can easily trick readers, especially if they’re only skimming. Tutorials exist for how to strip the telltale signs of AI use from your writing: Get rid of em dashes, colons, and of course the now-icky “It’s not X; it’s Y” formulations.

[Read: The typo vibe shift]

The problem is that the efficiency and frictionlessness that make AI appealing to writers are the same qualities that make it feel untrustworthy to readers. And readers are right not to trust it. No matter how much we may tell ourselves that AI is just a tool like spell-check, it isn’t. When we use AI to flesh out ideas, we lose the most important part of the writing process: thinking.

We tend to believe that efficiency is the highest virtue, the four-hour workweek the ultimate goal. Why sweat over the introductory paragraph of an essay if an AI program can sail over whatever argumentative obstacle you have in the space of 15 seconds? But the effort and the hang-ups are, as they say, a feature of the human thought process, not a bug. When human beings write, we judge ourselves; we stop; we backtrack. In published writing, the traces of this process are erased. But it is the process that makes human writing sensible and meaningful. Many authors describe how, when they’ve finally hit on the right idea, writing feels like going down a water slide; putting one sentence after another becomes easy.

When writing is hard, it’s often not just because we are tired, underfed, or inefficient but because our mind is trying to tell us crucial things. How many draft texts to colleagues or family members have we all stared at in frustration, wondering why they don’t feel quite right—until we finally realize that they need to be rethought completely, or not sent at all? When a book I was writing became an almost hopeless grind, I tore up 90 percent of the manuscript; it became a far more honest work for having been halted at a conceptual dead end, forcing me to turn back.

AI can’t make that kind of judgment. Even if the companies that design AI programs could make them reason like a human being—a project whose hubris is underrated, given that we don’t fully understand the mechanisms behind our own thought processes—they won’t. After all, users consistently say they want AI models to be agreeable, compliant. That means AIs are reluctant to do what the voices in our head do all the time: interrogate the validity of our premise; misunderstand us, forcing us to explain better; insist that a query is stupid; refuse to answer a question at all. In a study published in March, a group of Stanford and Carnegie Mellon University scientists found that top AI models affirm their users’ ideas 49 percent more than humans do in conversation. They also discovered that participants rated more sycophantic answers as “higher quality” and said a sycophantic attitude made them more likely to use AI again.

So we end up with canned perfection—writing that can’t really be argued with, because it has no underlying deliberative reasoning process, no train of thought. As I wrote on X recently, AI writing is almost impossible to edit, because even when it sounds plausible, a closer look will show that every element is equally off: The tone is bland; individual word choices are baffling; the structure lacks sense; key pieces of the argument are missing; facts are false. Working on AI text, as an editor, is like trying to operate on a body whose skin, muscles, veins, bones, and organs are all compromised. There’s nothing to leave intact, nowhere to begin.

[Read: AI-writing scandals are getting very confusing]

Recently I asked ChatGPT Pro—which I paid for to summarize financial spreadsheets; I’m not a hater—whether it was capable of viciously dismantling an argument I’d read about the stock market. “I’m tuned to avoid being casually cruel, contemptuous, or needlessly snide,” it explained. “That doesn’t mean I can’t be sharp, skeptical, funny, or sarcastic … I can absolutely be dismissive of bad arguments, flabby prose, obvious spin, bureaucratic nonsense, or market delusions wearing a little tech-futurist hat.”

I wanted to look under the hood a little, to understand the reasoning behind how the program chose its phrases. “Why would market delusions be wearing a ‘little’ tech-futurist hat, rather than a big one?” I queried.

The program was incapable of pausing to wonder whether it had chosen the wrong word. Instead, it sought to justify itself in increasingly bewildering ways. “Because ‘little’ makes it funnier,” ChatGPT Pro answered. “The market delusion wants to stride into the room as Prometheus; ‘little hat’ makes it look like a raccoon that found a conference badge.”

Why a raccoon with a conference badge? I pressed. Why not a dog, or a frog?

“Raccoons live in alleys, storm drains, garbage nights, and morally humid environments,” it wrote. “Frogs have existential agency, while raccoons have logistical agency. A frog at a VC afterparty seems like an omen. A raccoon at a VC afterparty seems like it got on the guest list … Think: alley behind a conference hotel, the loading dock of a casino, the VC afterparty where everyone is saying ‘alignment’ but meaning ‘exit liquidity’ … Alignment sounds like ethics, safety, shared goals, humanity, the future. It has dry-cleaned blazer energy … Exit liquidity is socially clammy.”

All of these sentences are grammatically perfect. They also make no sense. And all substantially AI-generated writing is like this, under the hood. ChatGPT Pro agrees, though it has no idea why. “What if I said the whole raccoon thing made no sense as a metaphor?” I asked the program.

“I would agree,” it replied, ever servile. “At first, the raccoon worked as a throwaway image … A metaphor can survive one or two explanatory layers. After that, it starts looking like a raccoon in a trench coat.”

This is the kind of communication we’re becoming surrounded with. Its infiltration into every domain of our lives can’t be stopped. Even people who don’t use AI will begin sounding more like it. (A preprint by the Max Planck Institute for Human Development found that in off-the-cuff verbal conversations, such as podcast discussions, people are already exhibiting “a measurable and abrupt increase in the use of words preferentially generated by ChatGPT—such as delve, comprehend, boast, swift, and meticulous.”) After all, we remain so much smarter than machines, so much subtler, and thus so much quicker to learn and pick up cultural cues. The difference in how we operate will be extraordinary, and not at all hypothetical. Ten years ago I composed a reconciliatory email to a boyfriend but never sent it, because I couldn’t get the phrasing right. Only much later did I realize I simply didn’t mean what I’d been trying to write. If I’d had an AI program to help me get over the hump, I’d be married to a different person. A much less suitable one.

Maybe human writing will become like cloth-aged cheese or handloom rugs, an artisanal product created effortfully. Maybe we will come to treasure older writing. Herman Melville, George Orwell, Toni Morrison—all authenticated. Writing like this will be a fossil record for a kind of thought process we buried without realizing it. The other night, as I was drifting off to sleep, a 19th-century poem popped into my head:

Wynken, Blynken, and Nod one night

   Sailed off in a wooden shoe,—

Sailed on a river of crystal light

   Into a sea of dew.

A children’s rhyme, but it had a new beauty.

Or maybe smooth communiqués that arrive on time and betray no confusion, doubt, or internal struggle—that polish up our images as affable, efficient, and universally, if superficially, wise—is what we want. But at least we should know what we’re sacrificing.

The announcement of the new “air defense” system was issued from Changzhou. A company called Photon Matrix Lab claimed to have developed a new technology for identifying and eliminating deadly threats mid-flight. A video on Indiegogo showed potential buyers how it works: After detecting a mosquito, the device fires off what looks like a blue-violet lightning bolt. When struck, the insect does not just fall straight down, no—it is more satisfying than that: Its body somersaults and tumbles out of the frame, bringing its career of vampiric air raids to a sudden end.

Photon Matrix Lab had my attention. Under normal circumstances, a mosquito lives for just a few weeks, and in that time, its wings will carry it a few miles or so, at most, from the pond or puddle of its birth—but for some reason, I am almost always within range of one. The bugs seem to have a primal knowledge of my whereabouts, and a craving for my blood that goes beyond mere thirst. In a span of minutes, they will perforate my skin 10 times with the dirty needles that protrude from their faces, and each micropuncture will swell up into an insomnia-inducing welt the size of a silver dollar.

We are a secret society, those of us who attract this torment. When we meet one another at a barbecue, we bond over our shared longing for the mosquito’s extinction. On behalf of my fellow victims, I decided to look into this new laser to see whether it might really deliver us from misery. I reached out to Photon Matrix Lab to arrange a call.

The mosquito-killing laser was not invented in China. It’s as American as the Model T or the Colt Revolver. Lowell Wood, an astrophysicist who was the architect of President Ronald Reagan’s Star Wars missile-defense system, first proposed the idea in 2006. He’d been invited to a brainstorm convened by Nathan Myhrvold, a polymath inventor. Myhrvold had served as chief technology officer at Microsoft before founding his own company, Intellectual Ventures, and had remained good friends with Bill Gates, who asked him to look into new technologies that might help prevent malaria.

Myhrvold, now 66 and still the CEO of Intellectual Ventures, is jolly and excitable in conversation. On a video call, he told me that he was immediately drawn to the idea of developing the laser system that Wood had proposed. Myhrvold thought the weapon could be safely used, because mosquitoes are so tiny. He marveled at their paltry biomass: “There’s maybe 450,000 of them or 500,000 of them in a pound—whatever it is, that’s a shitload of mosquitoes,” he said. (In fact, there are about 180,000 mosquitoes in a pound.) Killing just one wouldn’t require that much beamed energy, which meant the laser could be fired around people, dogs, and cats.

[Read: Notes of a mosquito hunter]

At the time, Gates was in his mosquito-net era, having come to realize that the insects are the most dangerous animals on Earth. The diseases they carry kill more of us on an annual basis than snakes, crocodiles, sharks, scorpions, polar bears, and all human murderers combined. The lethal nature of mosquitoes is ancient knowledge, encoded in some of our most sacred texts. In the Book of Exodus, the third deadly plague that God sends against Egypt is described as kinnim, a Hebrew word that is rendered in the King James Bible as “lice”—but which some early Greek translations seem to have taken to mean “mosquitoes.” A few thousand years later, mosquitoes remain a plague on six of Earth’s seven continents. In the tropics, the bugs will feast on human flesh year-round. In the summer, their range extends close to the poles. I have personally endured unholy swarms of them in the Siberian Arctic.

Myhrvold’s team built a prototype of a “laser turret,” and he showed it off onstage at a TED conference in 2010. He told me he thought that Disney theme parks, luxury resorts, and sports stadiums might be impressed and buy the turrets for their properties. If some big, early buyer could supply the team with enough revenue that it could keep working on the new technology, Myhrvold figured that it could be made affordable for hospitals and clinics in the developing world too. He also guessed that large farms might be among the early clients, so his team figured out what kind of laser it would take to kill a plague of locusts.

Or perhaps they’d try to tap the “Sharper Image market,” on the theory that the people who buy high-end gadgets are the same ones who might derive some thrills from zapping a mosquito. “At the very least, it could be an entertaining conversation piece for someone’s Fourth of July barbecue,” Myhrvold said. None of it panned out: “We had discussions with potential investors and clients, and we even got some term sheets, but the deals all fell by the wayside.”

The mosquito problem is only getting worse. In 1985, a breeding population of the black-and-white Aedes albopictus mosquito hitched a ride on a Japanese tire shipment bound for Texas. Nicknamed the Asian Tiger, it likes to bite ankles, and unlike other mosquitoes, which tend to hunt blood at dawn and dusk, it also does so in the late morning and afternoon. It’s a better flyer too, on account of its smaller size; compared with other mosquitoes, which seem to dog-paddle through the air, it has the grace of a hummingbird. By 1990, the Asian Tiger was in 15 states, and it’s been spotted in 40 today.

Yet it’s China and not the United States that might soon become the world’s lone mosquito-laser superpower. Last year, China suffered two of its largest outbreaks of dengue and chikungunya—mosquito-borne illnesses both—in its recent history. The country’s citizens tend to be enthusiastic about technology. Chinese scientists have recently tried seeding local ponds and lakes with fish that eat mosquito larvae, and they’ve deployed aerial drones to follow up on their progress. Lasers are a natural next step.

[Read: Your next mosquito repellent might already be in your shower]

Jim Wong, the inventor of the Photon Matrix Lab device, was not available for an interview, so I spoke with Lawrence Leng, the company’s director of sales. I asked whether the Indiegogo video of insects being lasered was authentic. (Some degree of showmanship has long been part of laser-turret marketing: One of the zapped mosquitoes from Myhrvold’s TED showcase was glued to a pin.) Leng claimed that the footage was real. He told me that Photon Matrix Lab has been buying thousands of target-practice mosquitoes from a company that’s situated farther up the Yangtze Delta. On TikTok, Photon Matrix posted a video of the device killing the mosquitoes at night and leaving only micro-puffs of smoke behind; the video has been viewed more than 70 million times.

Behind Leng, I could see people walking around in the office. “We now have 10 people in R&D,” he said, gesturing in their direction. He noted that the company has received almost 4,000 preorders through Indiegogo, at a price of $638 a device. “They’re mostly from your country,” Leng told me. “People in America hate mosquitoes so much.”

By the time I reached out to Myhrvold, he had already seen the viral videos from China, and he did not seem impressed. “Our laser had a 50-meter range; it was like artillery,” he said. The Chinese company claims only that its device can zap mosquitoes up to six meters away. “It’s more of a BB gun,” Myhrvold said. But that was just his first impression, and he said he’d want to have a closer look at the device before offering a full review.

He may be waiting for a while. Last summer, Photon Matrix Lab announced that its mosquito lasers would start shipping by the end of 2025, but Leng told me that they’re not yet in production. He said that the company’s design patents have been “approved” by the U.S. and the European Union, but he later clarified that those applications have merely been submitted. The company is also waiting on safety certifications from multiple agencies.

[Read: Shazam for mosquitoes]

But all hope is not lost for the mosquito-afflicted. Scientists are experimenting with other futuristic technologies, including genetically modifying the insects themselves. A team led by Andrea Crisanti at Imperial College London has used CRISPR to genetically engineer a variant of the African malaria mosquito Anopheles gambiae that could bring that entire species to the brink of extinction. The modified males can produce viable embryos, but some of their female offspring can’t bite or reproduce; their male offspring retain the same engineering and would pass the relevant genes to the next generation, and the next. In the lab, this reduced entire colonies to zero within a dozen generations. Luke Alphey, a professor of genetics at the University of York, told me that he’s been working on a technique that would make these kinds of interventions hyperlocal—they would wipe out a particular disease-spreading population, not a whole species.

I prefer an abundance-agenda approach to our global mosquito problem. After all, a unique opportunity is now within our grasp. For millennia, mosquitoes have been a problem to be suffered, not solved: Herodotus reported that at night, in the fields along the Nile Delta, the ancient Egyptians would climb into towers that rose above the bug line or, on the water, they’d wrap themselves in fishing nets, which doubled as mosquito netting. This was behavior befitting a superpower 2,500 years ago, but the U.S. and China can go much further. Both countries should be using full-blown industrial policy to fast-track their mosquito-killing technology. If we need an arms race to get it done, so be it. The 21st century will belong to the civilization that vanquishes the mosquito.

Bondu is a stuffed dinosaur that speaks 27 languages. It—or, more precisely, the AI chatbot embedded inside it—can also play games, help with homework, and patiently answer a child’s questions, even the really inane ones. Its “bedtime mode” includes breathing exercises and stories. Bondu, which costs $300 and comes in four colors, is marketed as a playmate, a confidant, a teacher, a quasi-caregiver. The ads take pains to talk up its safety controls, including an app that allows parents to review the conversations that Bondu is having with their child, as well as its ability to adapt to a child’s mood, interests, and age. And they emphasize, repeatedly, that the product is “screen-free.”

This is an odd and technicality-laden argument to make about an object that contains the kind of computing power that would have basically been science fiction even a couple of decades ago—sort of like marketing a hand grenade as “bullet-free.” But Bondu knows its audience. What the toy is might be less important than what it isn’t. In one testimonial posted to Bondu’s website, a girl who looks to be about 4 years old chitchats about baby animals with her Bondu, whom she has named Rosie. The video cuts to a mom sitting cross-legged on the floor and smiling into a front-facing camera. “Camryn truly loves sharing about her day with her Bondu,” she says. “And I love that it’s something she can interact with that isn’t a screen.”

Screen time can be a problem—the American Academy of Pediatrics says so; many early-childhood educators say so; well-meaning in-laws do too. Unfortunately, screen time also rocks, in that it is about the only way to occupy a child while you wash the dishes or have a little lie-down or go to work or do any of the other necessary or pleasurable activities life demands and invites. The one thing that feels more urgently worse than plopping a kid in front of the TV is the desperation that forces it. And then, later, the guilt.

According to a survey conducted last year by Lurie Children’s Hospital of Chicago, roughly half of the polled parents put screens in front of their kids daily, often because of issues securing or paying for child care. An even higher number—62 percent—felt guilty about their children’s screen time. In group chats and parent forums, parents admit to letting their kids watch Sesame Street in the kind of hushed tones that might lead you to imagine they’re giving their children black-tar heroin for breakfast. Some game out sophisticated avoidance strategies: skipping family gatherings with screen-addled grandparents; specifically choosing schools that ban devices. In the Facebook group “Screen Free Parenting Community,” which has more than a quarter of a million members, the mood is something between a radical activist organization and a support group. Last month, one of the moderators posted a freaky video of lobotomized-looking toddlers screaming after their tablets had been taken away. A few posts down, a mother who was a month out from delivering her third child in four years begged for help, or for forgiveness, for letting her toddler watch TV while she was taking care of the newborn. “I feel like an absolute failure,” she wrote. “I am scared about the repercussions this will have in the long term on my son. I pray that this isnt too late.”

[Read: The dumbest phone is parenting genius]

Commerce is to anxiety as mosquitoes are to standing water. So here we have a new class of “anti-screen-time” electronic devices that purport to entertain your kid just as much as a screen, without rotting their brain. Bondu is joined by several other stuffies that have been outfitted with large language models, and seem designed to capture children’s attention and their attachment. Among them is a teddy bear that tells AI-generated bedtime stories; a saucer-eyed blue thing that employs “interactive AI features that make it feel more alive than a traditional stuffed animal”; an alien that can supposedly comfort a kid through a nightmare; and a plush rocket ship whose chatbot is voiced by the musician Grimes, a self-proclaimed busy mom.

Other products aim to satisfy the longing for screen-free parenting in less invasive and lower-tech ways, though they are still far from analog. The Yoto Player and Toniebox are rival audio devices that play music and short stories. The Tin Can is a Wi-Fi-enabled phone designed to look like a ’90s-style landline; since debuting last year, it has regularly had a monthslong waitlist. There is a light-up “screen-free tablet,” and a talking robot that will teach your child about STEAM, and an AI-powered “smart” sudoku board that promises “no apps, no tablets, no screen time battles.” Major toy manufacturers are now getting into the market, too, adding 21st-century bells and whistles to 20th-century products, presumably in a bid to not be left behind. OpenAI recently announced a “strategic collaboration” with Mattel, and earlier this year, Lego introduced a line of “smart” bricks equipped with speakers, microchips, and LED lights.

No matter the approach, the sales pitch is similar. These toys make a point of eliminating many of the most psychically and aesthetically offensive aspects of phone- and tablet-based entertainment designed for children—the singsongy audio, the bizarrely planed cartoon faces, the abstruse algorithm, the infinite scroll. They are generally priced to convey attainable quality, and they tend to be aggressively marketed on Instagram: As soon as I started researching this story, I was bombarded by advertisements for them, right between the ones I was already receiving for smartphone-addiction tools made for people my age. The ads are careful to stress how educational these toys are, how fun they are—but not so fun that they will distract your toddler from building a meaningful life. If Sesame Street is heroin, this is methadone.

A few weeks ago, I ordered a Yoto. People in the Facebook group love the Yoto, as do many of my friends, and thousands and thousands of other people. I was hoping my son would, too, because, really, what my son loves is the television.

And the tablet. And playing stupid games on my phone while we wait for the subway. Once he stood so close to the TV for so long while I was doing work that I texted an ophthalmologist friend out of fear that he had permanently deep-fried his retinas under my supervision. Another time, he had to be dragged away screaming from a video display at a museum; only afterward did I notice that the exhibit was broken—he was staring slack-jawed at a screensaver. The tablet is the source of his greatest joy (when it is out) and his greatest heartbreak (when we take it away). The video with the screaming tablet kids could have been filmed in my house.

I do not know exactly how to feel about this. Like many of my peers, I grew up transfixed by screens too—but they were small, stuck to a wall or mounted on a desk, and designed to be gathered around as a family. The interaction was passive: no swiping, no skipping, no on-demand viewing, no advanced systems built by rooms full of well-paid people all working diligently to deepen rabbit holes and maximize time spent watching ads. YouTube arrived in 2005, the iPhone in 2007, and the iPad in 2010. In the decade-plus since, entertainment—for both kids and adults—has become much more algorithmic and much more absorbing. People are being directed by the technology, and not the other way around. The devices themselves have become better, more portable, and also completely essential to societal participation.

[Read: The attention-span panic]

And many of the adults who survived this transformation have begun to wonder what it did to them. They worry that they’re no longer able to think independently, live authentically. They fret about their lost ability to spend time productively and uninterruptedly—to read a novel, appreciate a sunset, blast through a spreadsheet at work. In recent years, some adults have started to engage in ostentatious acts of self-restraint to reclaim their attention spans and mental real estate, some by buying dumbed-down products designed to import the functionality but not the itchy pull of a screen.

The people who personally experienced the smartphone revolution are precisely those who now have young kids. They are passing down the anxieties of their generation, like every generation does. The parents with Bricks are buying their kids Tin Cans. They (we) are old enough to remember a different world, and to worry about what kind their children are growing up in. I spend an average of five hours and 22 minutes a day staring into my phone. I love my son’s mind more than anything on Earth—the last thing I want is for it to turn out like mine.

The Yoto works a bit like a first-generation iPod and looks a bit like an old-fashioned TV set, the cuboid kind that lived in the family room of my childhood home. (Like many screen-free products, it actually has a screen, just a janky one.) Much of its available narrative content—accessed by inserting playable cards into the device—also reminded me of the TV I grew up with: Although the hundreds of cards available for purchase in Yoto’s online store include music, original stories, and adapted children’s books, some of the most popular ones evoke IP that a child would know only from screenland—Mickey Mouse, Thomas & Friends, The Wizard of Oz.

That many of these products adopt nostalgic signifiers and model themselves off decades-old tech is intentional, I think, or at least a secret to their success. Of course we want the objects sucking up our kids’ attention to at least kind of feel like the ones that used to suck up ours, even if they are equipped with computing power never before possible. Of course we want to be reminded of the good old days, the days before children or adults had to think about screen time.

Those days were defined by a fair bit of benign neglect, and also by the freedom engendered by a life less documented. On average, contemporary parents work much more and parent much more than previous cohorts. Child-rearing has become more intensive, more public, and more competitive. For a certain set of parents, smartphones and the internet ushered in an era not only of always-on work, but also of endless opportunities to compare themselves to others. (The screen-free parents group, after all, is a space made possible by screens.)

Bondu, according to the ads, is useful as a playmate—but it will also get your child to practice piano. Tin Can advertises “no distractions,” which sounds appealing except that it’s not entirely clear what, exactly, a child needs to worry about being distracted from. Ingesting this sales pitch, I was reminded of the way grown-ups talk about their screen anxiety. The discussion seems to be predicated on two baseline assumptions: that there is a right and wrong way to spend one’s time, even when it’s for leisure, and that screen-free activities are inherently more noble.

[Read: Why are there so many ‘alternative devices’ all of a sudden?]

Gazing at a sunset is good; taking a picture of it for Instagram is bad. Reading a novel on your Kindle is okay; reading it in paperback is better; scrolling BookTok is worse. Katie Davis, who co-directs the University of Washington’s Center for Digital Youth, has noticed that many parents who feel guilt about letting their kids engage with screens tend to justify it by emphasizing the screen’s educational value. They say things such as, “Oh, but you know, my kid was building worlds in Minecraft and learning to code,” she told me, “because that type of thing is supposed to be okay.” Many of the people concerned about their screen-wrecked brain’s ability to focus at work are putting their kids in front of not-screen screens so that they, the parents, can do more work, probably on a screen. And all the while, they are worrying about their child’s future ability to be successful at work, probably also on a screen, probably because of something they read on a screen.

Personally, I don’t worry very much about what other parents are doing, nor about my son’s competitiveness in the labor market (he’s 3). But as I unboxed the Yoto in front of him, chirpily insisting that it was so cool, I did begin to wonder whom I was trying to convince, and of what. He listened to Peter Rabbit—which I first encountered as a picture book, so very long ago—and I thought about inheritance, and about what I was giving to him, and if I’d rather he grew up in thrall to screens or in thrall to screens and also beset by guilt. Then I got distracted by something on my phone and moved on.

Steven Rosenbaum has decided that the real villain behind the bogus quotes in his book is a chatbot. Earlier this week, The New York Times reported that The Future of Truth, Rosenbaum’s much-discussed book about how AI shapes reality, contains more than half a dozen fake or misattributed quotes. Rosenbaum pinned some of them on his use of AI. He claimed responsibility for the errors and said he was investigating what went wrong. By the time I spoke with him on Thursday, though, he was pointing his finger elsewhere. ChatGPT “fucked up the book,” Rosenbaum said.

Rosenbaum, a media entrepreneur and the executive director of the Sustainable Media Center, said he came to rely on AI tools as both a resource and a conversation partner while he worked on the book (which he also notes in the book’s acknowledgments). During our conversation, Rosenbaum struggled to reconcile AI’s sometimes staggering capacities with its penchant for head-scratching hallucinations—such as an imaginary quote from the tech journalist Kara Swisher that he included in the book without verifying it. In recent days, he has come to feel “seduced and betrayed” by AI, suggesting at one point that it might have undermined him on purpose. “Depending on your paranoia level, it’s either quirky or evil or sneaky,” he said.

It’s been a rough week for human authorship all around. On Monday, a viral post showed a Nobel-winning novelist seemingly admitting to using AI to sharpen her story ideas, before later claiming she had been misunderstood. On Tuesday, allegations mounted that the Trinidadian author Jamir Nazir had used AI to write “The Serpent in the Grove,” which won the Commonwealth Short Story Prize. By Wednesday, two of the other five prize winners had come under similar scrutiny. (The Commonwealth Foundation, which administers the prize, initially said in a statement that it had confirmed that none of the winning writers had used AI. Yesterday, the foundation issued another statement saying it “takes seriously the allegations” and was reviewing the evidence.)

[Read: This literary AI scandal changes everything]

Since ChatGPT arrived, automated writing has become ubiquitous: A recent working paper estimated that more than half of all new books released on Amazon now contain AI-generated text. Chatbots’ prose has generally been good enough to fool schoolteachers and inflate Amazon product ratings—not earn glowing blurbs from prominent authors and win literary prizes. Recently, something has changed. As AI tools have improved and gone mainstream, the technology has penetrated intellectual spaces once thought to be fortified against its advances. This spate of scandals is forcing a fresh reckoning over what to do about the crisis.

One response has been to call for a redoubling of efforts to root out AI writing and reinforce the stigma against it. If shame won’t stop people from using AI to do the hard work of writing, maybe ridicule will. In Defector, Patrick Redford derided the “pathetic behavior” of writers who use AI. “You idiots!” he wrote. “Those models are the enemy!”

Treating any use of AI in serious writing as taboo is understandable. Up until now, it’s been relatively easy to use the hallmarks of AI-generated prose as a proxy for shoddy writing and thinking. Maybe we can keep that up a while longer. As I read The Future of Truth, I ran across an unusual amount of clunky repetition, formulaic transitions, and perplexing passages. One particularly tinny paragraph begins, “As we delve deeper into the mechanisms of misinformation, it’s essential to understand how it not only proliferates but also profits.” I ran the 146-word passage through Pangram, an AI-detection tool that is imperfect but reputed to be less flawed, at least, than some others. It registered the writing as 100 percent AI-generated. When I asked Rosenbaum whether he had let AI write any parts of his book, he said, “Absolutely not.” When I mentioned the Pangram result, he said, “I’m not going to get into that game.”

The bigger challenge may be that “AI writing” is not just one thing. There’s a wide spectrum between text that is untouched by machine intelligence and writing that is concocted entirely by a chatbot. At the maximalist end, most of us can agree that a writer wouldn’t deserve a prize for typing, “Write a haunting, 3,000-word literary short story set in Trinidad” into Claude and then slapping his name on whatever it spits out. On the minimalist side, it’s presumably fine for a writer to do some Googling in the process of researching a piece that is otherwise entirely her own. Then again, what they find may still be imbued with AI: Google search is answering more questions directly via chatbot, and the results are turning up more AI-written web pages. Good information comes from primary sources, not synthetic text.

Generic chatbots have been joined by purpose-built AI research and writing tools that can carry out complex tasks. A growing number of professional writers, following the lead of software developers, openly profess to incorporating AI tools into their workflows. The tech reporter Alex Heath, for instance, trained a version of Claude Cowork to write in his style and crank out first drafts of his stories, as Wired reported in March. My own use of AI is comparatively primitive but worth disclosing here: In line with The Atlantic’s internal guidelines, I sometimes use chatbots like a slightly smarter thesaurus to suggest the most apt word to plug into a given sentence, and I occasionally ask them to suggest expert sources on a specific topic. I also use an AI-powered tool to transcribe interviews, backstopped by my own notes.

[Read: The human skill that eludes AI]

Exactly where to draw the line on acceptable uses of AI is not as obvious as it might seem. In Rosenbaum’s case, the scandal can’t just be that he used AI while working on his book, because he acknowledged that up front. He got in trouble because he had used AI badly, failing to check its work on a task at which it is famously unreliable. Or consider that The New York Times, which has endured a spate of AI writing scandals, maintains two different standards. Its freelancers can use AI tools for “high-level brainstorming” and almost nothing else. Newsroom employees are encouraged to experiment with what the paper’s guidelines tout as “a powerful tool that, like many technological advances before it, may be used in service of our mission.” The leading trade group for book authors, the Authors Guild, eschews edicts but warns of the ethical risks of various AI uses.

Condoning AI for research but forbidding any use of its prose might be the most intuitive stance. It is certainly the most convenient: We have no reliable way to tell when AI was used to brainstorm ideas, research facts, or help a writer shape the framing of a story. But as the neuroscientist Tim Requarth pointed out in Slate, it is those hidden uses of AI in the writing process that give rise to our most valid concerns. The real threat the technology poses is not the overuse of the word delve in academic papers or the profusion of strained metaphors in literary fiction. It’s that we lose something essential when we outsource to machines the hard work of discovering the truth and interpreting the world around us (or, in the case of fiction, the worlds within us). It’s that the biases embedded in language models trained on dubious sources and controlled by tech companies will seep into the narratives that shape our understanding of reality. Are we sure that using AI to turn a phrase is worse than using it to decide what to write about in the first place?

If nothing else, the pileup of scandals should force us to think more precisely about what it is we fear from AI writing. If the problem were simply that it was bad, then its steady improvement would be cause for relief rather than alarm. On the contrary, the problem seems to be that AI tools are getting too good, at least superficially, and that people are placing too much faith in them. Even though Rosenbaum cursed ChatGPT, he told me he couldn’t imagine giving it up. That feeling might pose a greater threat to writing than anything he lays out in his book.

It’s a weird time to be studying computer science. Recent grads have a higher unemployment rate than those in just about every other major—yes, even philosophy. The internet is littered with rants from newly minted programmers who can’t find work. On one such YouTube video, the top comment reads: “Your first mistake is not being born earlier.” Students, meanwhile, are fleeing the field. Undergraduate enrollment in computer science dipped by more than 8 percent last year, representing the largest absolute decline across any major in several years. The falloff at the graduate level—14 percent—was even more severe.

Learning to code was supposed to be a ticket to a good tech job. It wasn’t just Silicon Valley that spread the gospel of computer science: “Support tha american dream n make coding available to EVERYONE!!” Snoop Dogg once tweeted. Now the decision to major in CS is more complicated. Nowhere has AI refashioned work as dramatically as it has for programmers. Coding bots have become much more powerful over the past few years, and they excel at precisely the kind of programming that might previously have been delegated to entry-level workers. An Anthropic co-founder, Jack Clark, recently warned that “the value of more junior people is a bit more dubious,” as some 90 percent of the company’s new code is apparently now AI-generated.

The popular narrative around CS has flipped to such a degree that some Silicon Valley insiders are now actively discouraging people against the major. John Coogan, a co-host of TBPN, a popular tech-news podcast, recently asked if it would be a “contrarian move” to study computer science “at a time when coding jobs are going away.” But studying computer science is not contrarian, and the major’s waning relevance has been overstated.

It’s true that the work situation is more dicey than it once was. “Forget Python, study Plato,” The Economist advised students last week. But although the unemployment rate for new CS grads is spiking, they have a relatively low rate of underemployment—that is, comparatively few are working in jobs that don’t usually require a college degree. (Consider that nearly half of philosophy majors are underemployed.) When it comes to wages, new computer-science grads are also still significantly outearning their peers. One explanation for why CS majors have such high unemployment rates is that they may be less likely to settle for lower-paid roles. If you’re optimizing for earnings, trading software for Socrates might not make so much sense after all.

[Read: The computer-science bubble is bursting]

None of this is to dismiss the AI threat to software jobs. The aforementioned employment data tracks students who graduated in 2024. AI has improved significantly since then, and the capabilities are likely to continue to increase, allowing bots to take on more sophisticated work. But the decline of manual programming—that is, writing code by hand—doesn’t obviate the need for computer scientists. Even as AI tools become more powerful, leveraging bots to build reliable and secure software still takes training and expertise. With the AI revolution in full swing, we are hurtling toward a future in which even more of the global economy is mixed up with the software industry. If anything, the AI-ification of work seems likely to require more people who understand computer systems at a deep level. Across the tech industry, demand for mid- and senior-career engineers is rising. The trouble, then, is how to adjust today’s computer-science programs to equip students for work when the field is changing so fast—especially when entry-level coding jobs that once were guaranteed are now far less certain.

“I don’t know where the world is going,” Michael Hilton, a computer scientist at Carnegie Mellon University, told me, “but I know the things I taught three years ago are not the right things to teach today.” As bots have become more capable, Hilton keeps updating his curriculum—he encourages students to use AI for coding. Other professors are moving in the opposite direction. Valerie Barr, a computer scientist at Bard College, told me that in her introductory class, coursework is now mostly done on paper. “I’m back to how I taught in the 1980s, when we didn’t have laptops and there was one computer lab for the whole campus,” she said. Barr believes that students who learn coding fundamentals the old-fashioned way will be the ones to come out ahead. “You cannot make effective use of AI tools if you don’t know something about what you’re asking the tools to do,” she said. In much the same way, grade schoolers learn how to do basic algebra by hand before they are allowed to use calculators.

The split over whether to embrace coding tools points to a larger divide in the discipline: Is studying computer science about training students to be good software developers, or teaching them the computational theory that underpins the field? As coding becomes automated, we might see a further fracturing between the two domains. On the theory side, the AI boom has put a premium on highly skilled researchers with a deep understanding of machine learning. Future students may enroll in new AI-related majors that take the conventional CS major and then layer in more specialized AI training. Such programs already exist at several colleges: MIT introduced an AI major in 2022, and it’s already become the second-most-popular major on campus—behind computer science. And some students who are interested in CS for its own sake will still go deep in other non-AI subfields, such as cryptography. Today’s AI boom is possible only because people pursued neural networks when they were uncool.

At the same time, new courses could offer students an introduction to software development without the theoretical baggage and proof-writing they might have otherwise had to wade through. Geoffrey Challen, a computer scientist at the University of Illinois at Urbana-Champaign, plans to offer a new course this fall in which he will teach students to develop software “without writing, reading, debugging, or viewing a single line of code,” he told me. Northwestern is also slated to offer an “entry-level creative coding” class for students without technical backgrounds. For all the talk of AI-literacy programs that teach students how to use chatbots, the real innovation might be in developing courses that train students in basic software-development skills. Most colleges require introductory writing courses because it’s understood that clear written communication is an important cross-disciplinary skill—even for students who plan to study physics or math. Classes that teach students how to use AI coding tools could become commonplace, providing students of all backgrounds with a baseline software-engineering skill set.

The days of computer-science grads being all but guaranteed cushy tech jobs may be coming to an end, and the next few years will almost certainly be tumultuous as the job market continues to adjust. But we’re on the precipice of a new era when learning to develop software will be easier than ever, opening the door to students who might not otherwise have chosen to study computing. Perhaps a new golden age of CS education has only just begun.

Toward the beginning of the 2002 film Secretary, a domineering lawyer (played by James Spader) barges into the office of his assistant (Maggie Gyllenhaal) with evidence of a work infraction: a memo she has written that has “three typing errors.” Spader’s character spits out a reprimand. “Do you know what this makes me look like to the people who receive these letters?”

Setting aside that his screed turns out to be foreplay, Spader’s character was channeling a widespread cultural revulsion: Typos were the ultimate shorthand for careless work. A spelling mistake was proof that the writer hadn’t bothered putting much effort into a piece of correspondence, that their instructions or advice shouldn’t be taken seriously—and perhaps that the recipient shouldn’t invest time in reading their note at all.

More than two decades later, as AI-generated writing has flooded workplaces, social media, and dating apps, old hallmarks of sloppiness—typos chief among them—are getting a new gloss.

[Read: The problem with using AI in your personal life]

Some job applicants are intentionally adding typos to their cover letters to prove that they, and not an AI program, wrote them. Celebrities and CEOs are sending out error-ridden emails and Instagram Stories, and instead of getting a scolding, they are praised for sounding authentic. On some dating apps, where people are, somewhat absurdly, prompted to compose their profiles with AI, typos are apparently no longer an automatic repellent. Nicole Ellison, a University of Michigan professor whose 2006 study showed that dating profiles with spelling mistakes turn people off, now thinks people are warming to the Tinder typo. “A typo maybe signals that you actually do care,” Ellison told Time recently, “because you took the time to write it yourself.” A 2024 study even found that people view customer-service chatbots more warmly when they make and correct errors: A spelling mistake, it seems, is a kind of anthropomorphizing event.

A peculiar reconfiguration of what people consider careless writing is taking place. Although typos and other mistakes don’t suddenly mean that a piece of writing is good or praiseworthy, to some people, they are at least signs that it is worth reading. On a base level, many of us are willing to invest time in reading a long email if we sense that someone actually wrote it, line by line.

In England’s early-modern period, starting around the 1500s, readers understood typos to be inevitable technological blunders. Books were produced collaboratively; writers sent off handwritten manuscripts to printers, who transposed them onto a printing press before setting them to paper. In the process, errors were often introduced.

Authors and editors cataloged these mistakes in “errata lists,” paratextual documents that they slipped into the books after publication—a last-ditch attempt to control the reception of their work. In these documents, they might lambaste their printers to explain the circumstance of mistakes, Alice Leonard, a professor at Coventry University who wrote about typos in Error in Shakespeare, told me. Authors would say, “I wasn’t able to be in the printing house at the time of printing,” Leonard said, or even blame the printer and claim that “the printer was drunk, or the printer was absent, or the printer is useless.” Instead of diminishing the book’s validity, errata lists lent an air of credibility; at least, the thinking went, someone had taken the time to point out what was wrong.

Some writers reveled in printing missteps. James Joyce, whose Ulysses contained more than 200 spelling or grammatical errors in an early edition, called his typos artful experiments in language, “beauties of my style hitherto undreamt of.” By that time, though, he was likely already out of step with his peers: The widespread dissemination of typewriters seemed to recast the typo as a hallmark of individual laziness. With typewriters—and, later, personal computers—printed mistakes became a product of the writer’s failure to read their work closely.

[Read: A corrected history of the typo]

Today, of course, anybody can deliver supposedly clean writing by simply funneling their text through AI, which will churn out a version rife with strangely recurring words (delve), opening interjections (Here’s the thing:), and eerie grammar that’s almost too precise for a typical written exchange. The technological development is prompting people to embrace the old understanding of typos, forgiving misspellings as inevitable errors rather than treating them with scorn.

Even for celebrities, the occasional typo in a public statement is sometimes taken as proof that they are speaking from the heart. This spring, the singer Zara Larsson, who made an offhand remark in an interview that angered Taylor Swift fans, posted a defense in an Instagram Story that included at least two typos (among them a misspelling of physical as psychical). Her statement, free of any trace of a publicist or ChatGPT, came across as sincere. “I like this post because it’s littered with typos,” a host of the celebrity-commentary podcast Who Weekly noted at the time. “You can tell she wrote this herself.”

And no one seems to be accusing Donald Trump of writing his error-ridden Truth Social statements with AI. His press office has suggested that spelling mistakes are evidence of his excellence: A spokesperson for the White House recently told The Wall Street Journal, in response to a question about his frequent typos, “President Trump is the greatest and most authentic communicator in the history of American politics.”

Gone, apparently, are the days when the country’s most powerful leaders are expected to deliver flawless written communications. In an email released with the Epstein files, Peter Thiel called Davos, the Swiss town that hosts the World Economic Forum, “Davis,” according to the Journal. In a text that was made public in a Securities and Exchange Commission filing, Paramount Skydance CEO David Ellison referred to David Zaslav, the CEO of the company he was in the process of acquiring, as “Daivd.” And Jack Dorsey, the CEO of the payment app Block, sent an all-staff email about layoffs without capital letters. Business Insider recently went as far as to proclaim that typos are “the new status symbol” for corporate executives.

These executives may not all be thinking about authenticity; a stray typo could be an innocent flub, or it could simply underscore how little they care. But these moments of textual slippage are oddly refreshing amid the general AI overload. More than half of English-language LinkedIn posts are likely written with AI, according to a study by an AI-detection start-up, and so are many of those “feel good” posts that dominate Instagram and Facebook. A Brookings Institution survey last year of more than 1,000 adults found that 35 percent of respondents with a bachelor’s degree used AI to write or edit documents at work. Peter Cardon, a professor of business communication at the University of Southern California who researches AI in the workplace, has been surveying more than 420 randomly selected “knowledge workers” every six months since 2023. More than half of them, he told me, use AI “at least weekly” to write communications such as emails.

[Read: The human skill that eludes AI]

That these AI-generated emails invariably arrive with tidy spelling and grammar does not mean they are warmly received. Office workers have told Cardon that, on a pure prose level, AI-generated emails or project statements are easier to read than the average person’s writing style. Yet, according to Cardon, people are ultimately less likely to act on AI-generated emails. A 2024 Journal of Communication study found that people may engage less with narratives that they think are written with AI—a result that squares with Cardon’s own research about workplace interactions. If an employee suspects that their manager, for instance, is using AI, “they’re less likely to think that person is sincere; they’re less likely to think that person is caring,” Cardon said. “They’re even less likely to think that person is competent.” We know what our colleagues sound like, and we can tell when they send out, say, a thank-you note that they didn’t actually write. So what’s the point of clear prose if you don’t feel any more encouraged by the end of it?

This is not to say that everyone has let go of their rancor for typos. They may still be, to many, a paradigmatic writing sin. But for others, the typo resurgence could be clearing the way for the resuscitation of other, old-school symbols of sloppy writing. Perhaps people won’t turn up their nose as quickly at sentences with extraneous prepositions, verbs that disagree with their subjects, or adjectives where they don’t belong. Maybe overwrought prose or sentences loaded with adverbs will one day draw a little less derision.

Across history, hawkers of new communications technologies have expressed a desire to smooth out and speed up human conversation. But their products have a way of estranging their authors from the final output: Printing presses inserted errors that authors themselves didn’t make, and now AI systems create communiqués that sound nothing like the person sending them.

What many people are starting to look for in written communications, whether they’re from a co-worker or a pop star, is voice. They want to hear the distinct cadences of a CEO, an influencer, or a celebrity, so they can believe that they are reading something genuine. Centuries ago, authors wrote errata lists for the same reason job applicants intentionally place typos in their cover letters today—to resist the universalizing force of new technology, and to prove that there is a real human behind their work.

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Sam Altman did not seem to be having a good time. During the many days that he spent inside an Oakland courtroom, the normally cheery CEO of OpenAI—a guy who tends to be chipper even when declaring AI’s existential risks to humanity—appeared anxious, even distraught. When he listened to the proceedings in Elon Musk’s lawsuit against him, a weekslong trial that threatened to remove Altman from OpenAI’s board and functionally destroy the company, he frequently concealed his mouth with his palm, fidgeted with a water bottle, and leaned forward and stared at the floor. He kept looking back at the rows of reporters behind him. On the witness stand Tuesday, Altman repeatedly noted how Musk’s actions had “annoyed” him.

Musk, who helped form OpenAI as a nonprofit in 2015, alleged that Altman and OpenAI had violated the organization’s founding principles by seeking profits. He was requesting, among other remedies, more than $150 billion in damages, which Musk said he would donate to the OpenAI nonprofit. This morning, a nine-person jury delivered a unanimous verdict after less than two hours of deliberation: Whether or not OpenAI had done something wrong, Musk sued outside the statute of limitations, two to three years depending on the charge. And Musk could have known of any alleged wrongdoing, the jury found, well before. Altman has been granted some respite: OpenAI and the AI industry will continue along, unfazed, at least until Musk appeals the decision. (A second portion of the case, related to claims that Musk made under antitrust law, remains unresolved, although the presiding judge has said that his are “not very good claims.” Neither Musk’s lawyers nor OpenAI immediately responded to a request for comment.)

OpenAI swept the legal argument. But in another sense, basically everybody involved in Musk v. Altman came away looking petty, short-sighted, deceptive, or ignorant. During the dozens of hours I spent in the courtroom, sometimes lining up as early as 5 a.m. to secure a seat, there wasn’t much substance to be found. Frankly, at the end of it all, everyone had good reason to be annoyed.

Musk came off the worst in this trial, by far. The question before the jury was whether OpenAI’s for-profit arm had somehow broken a legal promise the organization made to Musk at the organization’s founding: “It’s not okay to steal a charity,” as Musk told the jury on the first day. This was a farcical notion based on any number of pieces of evidence and testimony presented at trial, not least of which being that in 2017, Musk himself was involved in discussions for OpenAI to raise more money by making a parallel for-profit arm. Coming into the trial, this was already an uphill battle for Musk and his lawyers. But even by those low expectations, the entire affair was a debacle.

As a witness, Musk was impish. When asked simple questions by William Savitt, one of the attorneys representing OpenAI, Musk rambled and avoided the issue at hand. When the lawyers asked for a yes or no, he bristled: “The classic reason why you cannot always answer a yes-or-no question,” Musk said from the witness stand, “is if you ask a question, ‘Have you stopped beating your wife?’” (“We’re not going to go there,” U.S. District Judge Yvonne Gonzalez Rogers interjected.) Later, Musk accused Savitt of asking improper questions, after which Gonzalez Rogers sharply cut in, telling the world’s richest man, “You’re not a lawyer.” Musk conceded but, after a pause, grinned and added, “Well, technically I did take Law 101.”

When Musk answered questions, he argued that OpenAI had sacrificed safe and responsible AI development by prioritizing profits. But when cross-examined about AI safety, Musk was unable to articulate any coherent arguments. Savitt noted that Musk’s xAI, a competitor to OpenAI, is a for-profit company, and asked if xAI presents identical dangers. “Yes,” Musk said, “I think it creates some safety risk.” Savitt then asked about basic AI-safety measures. Musk, who earlier had testified that he wants to avoid an AI “Terminator outcome,” was clueless. Asked about safety cards, for instance, Musk responded, “Safety card? Why would it be a card?” These are years-old, widely used, industry-standard documents that anybody who has worked at an AI company in the past five years should be intimately familiar with.

The following day, in a particularly withering exchange, Savitt went down the list of Musk’s other enterprises. Did he think that Tesla was making the world better? “Yes,” Musk said. And is Tesla a for-profit company? “Yes.” Savitt then asked these two questions about SpaceX, Neuralink, and X. For each of his businesses, Musk responded yes and yes. The same man who has a trillion-dollar compensation package from Tesla and may receive another from SpaceX was suing OpenAI for trying to make a lot of money. I wondered to myself, What are we doing in this courtroom again?

Despite winning in court, Altman didn’t come off all that much better. The first question from Steven Molo, one of Musk’s lawyers, to Altman was “Are you completely trustworthy?” With a puzzled look, the OpenAI CEO responded, “I believe so.” Molo asked if he had misled business partners, and Altman, after a pause, said, “I believe I am an honest and trustworthy business person.”

Altman’s evasive answers were significant because he has a long history of being accused by colleagues and business partners of being deceptive. Ilya Sutskever, a co-founder and former chief scientist of OpenAI, testified that during his time at the company, he had felt that Altman created an “environment where executives don’t have the correct information,” which is not conducive to AI safety. Multiple former OpenAI board members testified to similar effect in explaining why, in late 2023, they briefly fired Altman. (For his part, Altman wrote in a recent blog post that he is “not proud of handling myself badly in a conflict with our previous board that led to a huge mess for the company.”) When the judge excoriated OpenAI’s legal team for making contradictory arguments in separate lawsuits that she is hearing, Musk smiled and nodded. Musk’s legal team essentially hung its case on impugning Altman’s integrity, and Molo told the jury in his closing argument to imagine that they were walking over a bridge: “The bridge is built on Sam Altman’s version of the truth,” he said. “Would you walk across that bridge?”

The many texts, emails, and internal documents released because of the lawsuit, and the sworn testimony of current and former OpenAI executives, were hardly flattering for the firm— depicting a treacherous company culture that has nonetheless made its staff fantastically rich. Sutskever said that his stake in the company is worth some $7 billion, and Greg Brockman, OpenAI’s president and another defendant in the lawsuit, said that his equity is worth some $30 billion. Altman, who previously told the Senate that he has no direct equity in OpenAI, testified that through an investment fund run by the start-up incubator Y Combinator (which Altman used to be president of), he has an indirect financial stake in the firm.

The trial surfaced and produced countless other shenanigans: Musk apparently called an OpenAI employee a “jackass” for wanting to prioritize safety over speed, after which that employee was given a satirical trophy depicting a donkey’s butt. (During his own testimony, Musk denied yelling at someone and said he would have used such a word only in jest.) In a diary entry, Brockman had written that it would be “wrong to steal the nonprofit from” Musk and that doing so would “be pretty morally bankrupt, and he’s really not an idiot.” Sutskever, a Yoda-like figure in the AI world, described AI progress from 2018 to now as “the difference between an ant and a cat.” At the beginning of the trial, the judge had asked Musk to refrain from posting on social media about the trial as it unfolded, and he did show restraint. Immediately after the verdict, though, Musk posted on X: “The ruling by the terrible activist Oakland judge, who simply used the jury as a fig leaf, creates such a terrible precedent.”

To the extent that the trial could have actually been about the best way to develop AI for the benefit of humanity, and about whether OpenAI is honoring its founding pledge to do so—well, it simply wasn’t. For the most part, Musk and Altman—billionaires who are perhaps the two most influential tech CEOs in the world—were in essence asking their attorneys to debate whether making ungodly sums of money was acceptable. In a remarkable exchange during closing arguments, Gonzalez Rogers excoriated one of Musk’s lawyers for misleading the jury: Molo, after attacking the bridge “built on Sam Altman’s version of the truth,” said that Musk is not asking for money from OpenAI. The district judge pointed out that he, in fact, was asking for money. “You need to retract that statement, or you need to drop your claim for billions of dollars,” the judge said. Musk’s lawyers did not drop the demand.

Anthony “Bingy” Arillotta waited years to become a made man in the Genovese crime family, and when at last the call came in August 2003, he followed directions to the letter. According to sworn testimony, Arillotta was summoned to a steak house in the Bronx, where he was made to hand over his cellphone, beeper, and jewelry before being driven to an apartment building. When he got there, he was taken to a small bathroom and strip-searched for electronic devices. For his big meeting with the boss, he was given a bathrobe to wear.

Until recently, only spies and criminals had to worry this obsessively about their private statements being picked up by electronic equipment. But soon, the average person might need to deploy surveillance countermeasures. The next time you conduct a delicate bit of office diplomacy or share a romantic or financial secret with a friend over drinks, a sensor built into someone’s glasses, necklace, or lapel pin might be watching you and listening.

In March, the tech start-up Deveillance announced the development of Spectre I, a hockey-puck-shaped device that purports to prevent others from recording you (no strip search required). The company was founded by Aida Baradari, a recent college graduate who was worried by the surge in people wearing AI-enabled recorders. These wearables can be used as a silent notetaker, a personal assistant, or even a therapist of sorts. That technology isn’t yet mainstream, but it may be soon. Apple—the company with the largest personal-tech ecosystem in the world—is rumored to be developing an AI pin or pendant that would serve as an iPhone’s constant eyes and ears; many other products of this type are on the way. AI accessories could one day be as widespread as AirPods.

New surveillance technologies tend to breed new countermeasures, which lead, in turn, to more sophisticated surveillance. During the Second World War, after Germany operationalized radar, the Royal Air Force began dropping thin strips of metallized paper cut to a specific size that resonated with the radar, swamping German screens with phantom echoes that were indistinguishable from real aircraft. Some historians have argued that the ensuing radar arms race was more consequential to the war’s outcome than the Manhattan Project.

For decades, crude jammers have been sold to people who hope to avoid being recorded. Early versions blasted loud, unpleasant white noise to conceal voices. More recently, companies have made models that emit a steady stream of ultrasonic sound at inaudible frequencies, exploiting a quirk of microphone hardware that converts those high frequencies into noise. In 2020, a team at the University of Chicago led by Yuxin Chen reported that it had mounted 23 ultrasonic transducers on a single bracelet, such that jamming signals could be sent in all directions instead of being focused on a single target.

[Read: The most reviled tech CEO in New York confronts his haters]

But even high-tech jammers have a hard time fending off today’s AI wearables. The most advanced pins, pendants, and glasses use speech-recovery algorithms to strip away unwanted noise, whether it originates from everyday sources—such as the clinking of glasses in a crowded bar—or from an ultrasonic jammer. This task the algorithms perform is quite difficult: In that crowded bar, a microphone on a person’s lapel will intercept sound vibrations from many different sources at once. It will pick up a bartender calling out a drink order, music emanating from a speaker, bursts of laughter coming from nearby tables—and all of these sounds ricochet off of walls and other objects, creating yet more noise. The human body solves this “cocktail party problem” without us noticing: Our ears serve as dual microphones, and our brain can use the timing and intensity differences between them, along with layered processing in the auditory cortex, to isolate the voice of a person who is sitting across from us.

DeLiang Wang, a computer scientist at Ohio State University, has spent decades training neural networks to accomplish that same goal, for the purpose of improving hearing aids. By feeding the networks hundreds of hours of recorded human voices, he has taught them to recognize the frequencies and rhythms of speech. The models build an internal representation of “speech-ness,” and when they encounter a noisy recording, they focus on the parts that match the patterns they have learned and then suppress everything else. The most advanced technologies can now infer missing syllables in the way that a reader fills in a redacted word from context, allowing them to reconstruct speech that wasn’t cleanly captured in the first place.

Big tech companies are trying to do this too. Microsoft has been running an annual Deep Noise Suppression Challenge since 2020 to advance the field. (Their in-house team is trying to make Teams meetings less excruciating.) Other companies are working on noise cancellation for cellphone calls and podcast software. This sort of research is meant to improve the lives of normal users of technology—assuming that we podcast listeners count as normal—but every advance in de-noising can also be used to help an AI assistant recover speech from a jammed recording.

Defeating these algorithms may require a different countersurveillance approach altogether. Finn Brunton, a historian at UC Davis and the co-author of Obfuscation: A User’s Guide for Privacy and Protest, told me that one of the best ways is to identify the data that a device is trying to collect, and then supply it with a junk version. The Berlin-based artist Adam Harvey used this strategy when he developed makeup and clothing that frustrate facial-recognition algorithms. Daniel Howe and Helen Nissenbaum did something similar with a browser plug-in called TrackMeNot: Rather than concealing a user’s Google searches, the extension continually runs its own randomized decoy queries in the background, so that whatever a user actually searched for becomes lost in a sea of false leads.

People have tried this technique in the realm of audio too. Woodrow Hartzog, a law professor at Boston University who studies privacy and surveillance, told me that early in his legal career, he worked with defense attorneys who worried that their jailhouse conversations with clients would be recorded. To fight back, they played “babble tapes”—audio files layered with 40 tracks of voices in different accents—in the background.

In 2023, a team led by Ming Gao, now a researcher at Nanjing University, used human voices to defeat speech-recovery algorithms in a different way. Its jammer, called MicFrozen, is worn by a speaker who doesn’t want to be recorded. It listens as they talk and then generates a real-time stream of ultrasonic “anti-speech” tuned to the speaker’s voice, much like the noise-cancellation technology in your headphones. The device then sends out another layer of counterfeit speech-shaped sound to mislead any algorithm that tries to reconstruct what was lost.

Baradari, whose company is working on the Spectre I device, wouldn’t tell me exactly how her jammer’s signals work, but she said that they, too, resemble speech. The launch video for Spectre I claims that the device will also be able to detect the presence of nearby microphones. When I asked Baradari how it will do that, she clarified that her team is still “working on that part right now.”

However effective Spectre I turns out to be, it won’t be the end of the recording arms race. More capable AI models may eventually deploy some new listening tricks of their own. They may bypass recorded audio altogether. In Stanley Kubrick’s 2001: A Space Odyssey, when two astronauts retreat to a soundproofed pod to discuss disconnecting HAL 9000, the ship’s computer simply reads their lips through the porthole. A wearable powered by a model that’s been trained on enough conversation footage could, in principle, do the same. In theory, it could also stare at a glass of water between two people and recover their speech from vibrations on the liquid’s surface.

AI wearables may always have an edge over countermeasures. After all, they’re using a technology that is a product of the entire speech-processing industry, which takes in billions of dollars in investments—not just for AI assistants but also for hearing aids, smart speakers, and teleconferencing tools. Meanwhile, only a few academics and small companies are defending us from these technologies. “The thing about cat-and-mouse games is that we know how they usually end up for the mouse,” Hartzog said. “And in this case, the cat includes some of the most powerful corporations to ever exist.”

The Mafia knows what it’s like to be a mouse. By the time Arillotta, the aspiring made man, was told to put on the bathrobe, criminal organizations had been engaged in surveillance arms races of their own for decades. After law enforcement started bugging their phones, bosses would conduct business in person. Sometimes, they’d use a safe house or a vehicle, but those could be bugged, too, and so sensitive information might have been communicated only during a walk-and-talk. Eventually, crime families turned to burner phones, and then devices with encryption. But here, again, they fell prey to the cat.

In 2018, the FBI began secretly running Anom, its own encrypted-phone company. Through informants, it sold 12,000 devices with a special Anom messaging app. Members of Mafia families, motorcycle gangs, and other criminal organizations treated the phones as a status symbol, and used them to negotiate drug deals, launder money, and participate in all manner of other illegal activity. But the security that they offered was a ruse: Every message that they sent was being intercepted by the feds.

AI has ascended to the role of main character. When Donald Trump traveled to Beijing for a historic summit last week, AI was one of the central topics of his discussions with Xi Jinping. As the two nations remain locked in a technological arms race, the president brought along some of the United States’ most powerful AI executives, including Elon Musk and Nvidia’s Jensen Huang. A continent away, the European Union has been unsuccessfully petitioning Anthropic to grant access to its advanced cybersecurity model, Mythos. Back in the United States, millions of students and teachers are dealing with the fallout of a devastating ransomware attack on the software platform Canvas—a hack that was likely aided by AI tools. And on Thursday, Cisco became the latest major company to justify layoffs by pointing to AI.

The past six months have marked a sea change in the reach and influence of AI. For most of 2024 and 2025, there was talk of AI progress slowing down or even stopping altogether. Even as the technology began to infiltrate schools and reshape financial markets, AI was relatively easy to compartmentalize from other major, more pressing issues in American life.

No longer. Now the technology has become regarded as a matter of the greatest economic, political, and global consequence. The most important issues in U.S.-China relations? Tariffs, Taiwan, and AI, apparently. Political leaders and pundits including Bernie Sanders and Steve Bannon have put AI center stage, and the backlash against data centers is loud and inescapable. The specter of AI-driven layoffs hangs heavy—as does the threat of advanced hacking bots capable of taking down electrical grids and breaking into banks. All manner of once-speculative concerns about AI have become pressing matters. There is no longer a distant AI future so much as the mess we are all forced to confront today.

The newly chaotic and inescapable state of AI is the result of two inflection points. The first came at the start of the year, when AI agents exploded in popularity. Products such as Anthropic’s Claude Code and OpenAI’s Codex don’t just talk to you; they can do things on your behalf—code, trade stocks, analyze spreadsheets, generate slide decks, and even create Amazon listings. The technology’s once-questionable economic value became very clear, very quickly, to a large number of businesses, which have clamored to incorporate agents alongside, or in lieu of, their human employees. As agents have swarmed the workplace, nearly three-quarters of employed Americans think AI will decrease overall job opportunities and 30 percent of Americans are concerned that AI will make their own job obsolete.

The second shift began in late February. First, a high-profile contract dispute between Anthropic and the Pentagon revealed how essential AI has become to national security. Then, in early April, Anthropic announced Mythos, a model with the ability to rapidly find and exploit bugs throughout the internet. (Shortly after, OpenAI came out with an analogous model.) In tandem, these events suggest that some of the most catastrophic fears about AI could come true: Several independent cybersecurity experts have told me that these models are approaching the abilities of the most elite human hackers. Anthropic and OpenAI have not released these cybersecurity models to the public, out of fear they will be used by criminals or terrorists; meanwhile, companies and government bodies alike are hungering for access so they can use the tools to patch any bugs. As a result, AI labs have become major geopolitical actors in their own right.

Spurred by the threat of massive AI cyberattacks, the Trump administration is now reportedly weighing the possibility of testing or even licensing the most powerful AI models before their public release—moves the White House once called “dangerous” and “onerous.” White House Chief of Staff Susie Wiles is said to be spearheading Trump’s AI policy and has written a rare post on X vowing to keep Americans safe from AI cyberattacks by ensuring “the best and safest tech is deployed rapidly to defeat any and all threats.” (A White House official told me that “any policy announcement will come directly from the President.”) This month alone, dozens of members of Congress have signed letters to the White House on AI regulation.

It’s hard to overstate the extent to which AI has crept into contemporary life, even for people who aren’t commonly using the technology. A poll this spring showed that, for Americans, AI is growing in importance faster than any other issue. AI wasn’t a focus for campaigns in 2024, but several races coming up this year are poised to involve heated debates over the technology. Data centers in particular have gone from basically invisible to a divisive issue that cuts across party lines: 70 percent of Americans oppose the construction of an AI data center in their community. These centers’ voracious demand for natural resources might be showing up in your electrical or water bill or your receipt at the gas pump. Data centers have also become objects of military and political violence. Last month, the home of an Indianapolis city councilman was shot up after he voted to approve a data center. And these buildings have been targeted or threatened by Iranian, U.S., and Israeli forces during the war in the Middle East.

There will never again be a graduating class that experienced even a year of college without ChatGPT. On Instagram, Facebook, and X, influencers preach about how to use Claude and ChatGPT to make your life easier. Recent leaps in deepfake tools make it harder than ever to assume that any given post on social media is human-made. As if AI had not already eaten the economy, Anthropic and OpenAI are racing to be listed on stock markets in what will likely be two of the largest public offerings in history. This will dramatically warp the public-investing landscape and affect, for better and worse, basically anybody with any sort of savings—a college fund, a 401(k), a pension.

All of which is to say, basically anything that is American seems tangled up with AI: the war in Iran, gun violence, the midterms, NIMBYism, falling test scores, class inequality, the stock market, housing, gas prices. None of these issues are necessarily determined or superseded by AI—far from it—but rather, this technology and industry are now directly, unavoidably implicated in them all. And the experience of this AI-saturated present is a bewildering one. Partisan lines on AI are scrambled and confused. The influx of cash into data centers has propped up the U.S. economy, making it impossible for economists and policy makers to fully understand the effects of tariffs and the war with Iran. More and more companies are citing AI for mass layoffs, but whether this is a genuine justification or a convenient excuse to downsize is anybody’s guess. Whether AI is going to empower or rot all our brains, too, will only become evident many years from now. All these questions and tensions are hard to make sense of, let alone resolve, but they can no longer be deferred.

The path here was not the inevitable result of some technological, scientific, or economic law. Nor is continuing down it. To the extent we are already living in the AI future, it is the result of a series of calculated decisions by the biggest tech firms and their investors. Silicon Valley has spent ungodly sums on AI and data centers: Microsoft, Amazon, Meta, and Google alone have already spent more on data centers since the launch of ChatGPT than the federal government spent to build the entire interstate highway system. Those expenditures are set to grow, even as consensus opinions on whether all this spending constitutes an economic bubble fluctuate every few months. Meanwhile, AI companies have been hard at work partnering with local and federal government agencies, major colleges and research universities, Fortune 500 companies, and media organizations to weave their products into everyday life.

All of this spending and all of these partnerships were set in motion years before the technology was actually capable or reliable enough for widespread usage. Now these same companies are barreling forward to consummate their technological revolution. For everyone else, the AI future is beginning to feel less like something you participate in and more like something that happens to you.

Updated at 5:06 p.m. E.T. on May 19, 2026

One afternoon earlier this year, my 11-year-old son was sitting at his laptop and working quietly on his math homework. At least, that’s what he was supposed to be doing. When I glanced at his screen, equations were nowhere to be seen. He was controlling a monster in the midst of battle, casting magic spells to outduel an opposing player.

“That’s not your math homework!” I told him. But it was. His fifth-grade-math teacher had told her students to spend time on Prodigy, a site that looks and feels like a video game. As my son indignantly showed me, Prodigy surfaces multiple-choice questions in between cartoon-monster attacks. Correctly identify an isosceles triangle or the square root of 49, and your “Aquadile” or “Bonasaur”—barely veiled rip-offs of Pokémon characters—gets a health boost that will help it fend off your opponent’s next salvo.

Prodigy is among a bevy of gamified tools that have gained a foothold in classrooms across the country by promising to make learning fun. (As Prodigy’s website puts it: “Kids no longer have to choose between homework and playtime.”) These platforms—which also include Blooket, Gimkit, and Kahoot—can seem like a win-win. Students’ eyes light up at math-and-vocabulary-review sessions that once induced groans. Teachers, meanwhile, can use the games to track which questions kids get right and wrong, helping them triage trouble spots.

But as I watched my son play Prodigy, it became clear there wasn’t much learning happening. In about 10 minutes of gameplay, he spent less than 30 seconds answering math questions. When he got one wrong, the game didn’t pause to diagnose where he went wrong or guide him to the correct answer. The only time he slowed down, grudgingly, was when Prodigy forced him to watch videos advertising its paid-membership plans.

Other popular ed-tech games also lean into gaming more than learning. Gimkit lobs occasional multiple-choice questions in the middle of live, multiplayer games that closely resemble popular commercial titles such as Among Us and Only Up. Blooket offers a single-player game similar to Plants vs. Zombies that can be used as a homework assignment and others, such as Gold Quest, that are designed to be played live by a whole classroom. While parents and teachers fret over students’ watching MrBeast videos during social-studies class, schools have embraced education software that has become hard to distinguish from Candy Crush.

Educational games have been around for decades; Millennials may remember playing Math Blaster and Oregon Trail in computer lab. Only recently have web-based, free-to-play platforms become a staple of daily lesson plans and homework assignments. Their rise has been abetted by the prevalence of school-issued Chromebooks and an incursion of technology into almost every aspect of education since the pandemic. For kids the age of my son, who attended kindergarten on Zoom, a school experience mediated by ed tech is all they’ve ever known.

Some of these platforms are now so compelling that students want to play them in their spare time. Blooket, for example, has a gambling-like feature that has proved popular throughout the gaming industry: Players earn an in-game currency they can spend on packs that offer a slim chance at rare prizes—in this case, special avatars, or “Blooks.” The site has spawned a cottage industry of YouTube streamers who share hacks for obtaining more currency and post screen recordings of their luckiest “pulls” from reward packs. “Oh my God, we pulled it,” one popular YouTuber raves in a video that has nearly half a million views. “One of, if not the, rarest Blooks in the game. And if this video gets 10,000 likes, I’ll give it away to one of you guys.”

Ben Stewart, who co-founded Blooket as a high-school student in 2018, told me that the company now has about 20 employees, millions of active users (he wouldn’t say exactly how many), and 23 game modes. He understands that some teachers and parents might have qualms with education software that mimics the addictive mechanics of mobile games. Blooket is designed not to supplant lectures or project-based learning, he argued, but rather to replace flash cards and worksheets as a way of reviewing facts that students have already absorbed. “In our mind, if you’re using Blooket for an hour in a class, something has gone wrong,” he said. Blooket aims to surface questions at least once every 20 seconds, he added, and limits the amount of rewards players can earn in a day (though they can spend money to unlock more). Prodigy is also not meant to be used as a substitute for instruction, Rohan Mahimker, the company’s co-CEO, said in an email. “Balancing gameplay with genuine learning is central to how we build Prodigy and why we’ve invested so much into curriculum and pedagogy,” he said.

Several teachers I spoke with agreed that Blooket and its ilk are best deployed in small doses and for defined purposes. Mashfiq Ahmed, a high-school-chemistry teacher in New York City, told me that he uses Blooket and Kahoot for review sessions at the end of a unit, and as filler for a substitute teacher when he’s out sick. Ed-tech games also allow kids who finish their in-class assignments early to work ahead on their laptop, keeping them quiet and out of trouble until the bell rings. And if nothing else, they can provide “a quick blast of competitive entertainment,” Jason Saiter, a high-school teacher in Dublin, Ohio, told me. “Sometimes teachers need things like this to get through the day. Sometimes certain types of students do too.”

But things can sometimes get out of hand. On Blooket and several other platforms, students can create their own quizzes from existing templates. Some have cleverly learned to design them so that any answer is designated as correct—they simply mash the first answer to each question as soon as it appears to maximize their in-game rewards. The internet is full of hacks for Blooket, Gimkit, Prodigy, and others—such as browser extensions that automatically answer every question correctly. When I ran this by Stewart, he flashed something between a smile and a grimace. “Kids are creative,” he said. “They try to cheat our games as many ways as they possibly can.” If there’s one thing that all of these years of tech-centered education has taught schoolkids, it’s how to game the system.

Over the past few years, districts across the country have enacted phone bans or restrictions in a bid to limit distractions. Schools have also blocked students from using their laptop to access sites such as YouTube and Roblox. But those measures don’t solve the deeper problem: Software has eaten the American school, and unwinding that will require more than a content filter or a Yondr Pouch.

Some parents now want to go further. Jodi Carreon, a mother based in San Marcos, California, told me that her younger child was in second grade when he began coming home begging her to pay for Prodigy’s premium service so he could get more rewards. Then she started getting notes from teachers that her son was getting distracted playing Prodigy in class. “I’m like, ‘You literally handed them this,’” she said. Carreon is now the national-expansion director for Schools Beyond Screens, a parent group that recently successfully pushed Los Angeles to become the first major U.S. school district to adopt sweeping restrictions on laptop and tablet use in classrooms.

Other experts argue that the problem isn’t games or technology per se—it’s the thoughtless way that schools are using them. A well-designed game “can be extremely effective in not just getting kids interested in the subject matter, but to help them understand why they’re doing it in the first place,” Jan Plass, a professor of digital media and learning sciences at NYU, told me. He cited a 2008 game called Immune Attack, developed in part by scientists, in which players must navigate a nanobot through a patient’s bloodstream to spur their immune system to fight off infections. He contrasted that with gamified tools such as Prodigy, which simply bolt multiple-choice questions onto unrelated game templates. It’s a lazy approach, but it’s cheap and accessible, and it dovetails with an education system geared toward standardized tests.

In other words, the status quo of ed tech is bleak. Screen time has become a default rather than an intentional choice for harried teachers and distracted students. That day I first encountered my son playing Prodigy, I noticed something odd after several minutes of watching him. He was learning how to divide fractions in math class, but the screen kept flashing addition problems. “Oops,” he said when I pointed that out. “I must have clicked the wrong lesson.”

You hear wild stuff all the time now. Like this story that Nat Friedman, a former CEO of GitHub, told recently at a conference. Friedman uses OpenClaw, an autonomous AI agent that runs on his computer, acting like a personal assistant. One day, his OpenClaw decided that he wasn’t drinking enough water, so Friedman instructed the agent to “do whatever it takes” to make sure he stays hydrated. According to Friedman, eventually the bot directed him to go to the kitchen and drink a bottle of water. It informed him that it was monitoring him via a connected camera in his home. “I’m going to watch to make sure you do it,” the bot supposedly said. Friedman did as he was told, and, moments later, the bot sent him a frame of him drinking the bottle of water and said good job. “I felt like I did do a good job,” Friedman said.

The world is only a few years into the AI boom, and this strange brew of hype, utility, and creepiness is commonplace. On X—arguably the beating heart of AI insider discourse—investors, influencers, programmers, researchers, podcasters, and countless hangers-on reach out across the algorithm to shake you by the shoulders. Claude “broke down my entire life with eerie accuracy. No horoscopes. No tarot. Just pure AI,” one post reads. Another crows: “Our team is stunned. We gave Claude Opus 4.6 by @AnthropicAI $10k to trade on @Polymarket. It’s now has an account value of $70,614.59.” The post includes a graph with a small asterisk that notes that this trading was part of a trading simulation and not done with real money.

A defining feature of all this evangelizing is its frenetic pace. If you are not paying close attention to the daily AI discourse, a lot of the conversations are almost unintelligible. From week to week, narratives whipsaw. A new prompt seminar “WILL CHANGE HOW YOU BUILD WITH AI FOREVER”; no, wait, prompting is dead. Claude “CHANGES EVERYTHING”; actually, it’s all about OpenAI’s Codex now. Get in, loser, we’re vibe-coding websites. Scratch that: We’re vibe-trading now—earning money while we sleep.

It all moves so fast that veterans of the AI discourse jokingly yearn for the good old days … of 2022.

I’ve written previously that one of AI’s enduring cultural impacts is to make people feel like they’re losing their mind. Some of that is attributable to the aggressive fanfare or the way that the technology has been explicitly positioned to displace labor. But lately, I believe, it’s the accelerated nature of the AI boom that’s driving people everywhere mad. Both the conversation around the technology and its implementation are governed by an exponential logic. Intelligence, revenues, capabilities—all of it is supposed to hockey stick, say the boosters. New, supposed breakthroughs are touted but then immediately couched with the reminder that this is the worst the technology will ever be. Because AI systems have bled into every domain of our culture and economy, it's exceedingly difficult to evaluate the effect of the technology outside of a case by case basis. That you can’t begin to wrap your mind around the AI boom or orient yourself in it is a feature, not a bug, for those building the technology. But for anyone just trying to adapt, it’s difficult not to feel resentful or alienated. Silicon Valley is trying to speedrun the singularity, and it’s polarizing the rest of us in the process.

The whipsaw itself has existed for several years. Since the arrival of ChatGPT, the AI boom has toggled around an “It’s so over”–“We’re so back” axis, with the industry seeming to fall short of its own mythology, then announcing yet another paradigm shift. But the latest shift from chatbots to coding agents—self-directed tools like the one that apparently minded Friedman’s hydration habits—has turbocharged this churn. Boosters see the agents, unlike chatbots, as a convincing step toward the predictions of AI executives that the technology could eliminate untold white-collar jobs and rewire the very nature of work. Adoption and usage of models such as Claude Code and OpenAI’s Codex have skyrocketed, alongside revenues. Bubble talk (for now) has chilled out, and CEOs are saying things like “Think of this as the dawn of a new Atomic Age.” We’re so back.

In AI research, a popular sentiment is that a “jagged frontier” exists in AI utility and adoption: AI tools can be extremely, unexpectedly good at some human tasks and extremely, unexpectedly bad at others. As this frontier becomes even more jagged, it appears to be pressing people deeper into their previously held opinions of AI, such that AI evangelists and skeptics are living in different worlds. On Reddit and LinkedIn, workers are lamenting managers who have cute names for their bots and who mandate that every marketing summary be run through Microsoft Copilot. Some of those workers say they are writing their memos, pretending to be chatbots, just so they have some agency in their job.

Elsewhere online, programmers are beginning to describe an affinity for coding agents that is veering into unhealthy territory. “I’m up at 2AM on a Tuesday,” Anita Kirkovska, the head of growth at an AI company, wrote recently, “not because I have a deadline, but because Claude Code made it so easy to keep going that I forgot to stop.” She describes a “competence addiction” caused by the tools making her so productive: “You hit a prompt, the agent succeeds, you get a dopamine hit. The agent fails spectacularly, you get adrenaline. Both are reinforcing. Both keep you at the terminal.” Kirkovska argues that she sees this among all kinds of AI power users—an unsustainable flow state in which decision making begins to falter and people become sloppy as they grind away.

MIT Technology Review’s Mat Honan describes the feeling that too much is changing, too fast as “AI malaise.” You’re starting to see it in surveys—a recent Gallup poll finding that only 18 percent of Gen Zers said they felt hopeful about AI (a drop of 9 percent in the past year), or an NBC News survey showing that AI has a favorability rating of 26 percent. It’s bubbling up in the physical world—in the 20 data-center projects canceled because of local opposition in the first quarter of this year or in a college-commencement ceremony at which students booed a speaker extolling AI as “the next Industrial Revolution.” You can see it in a few isolated, and inexcusable, acts of violence, such as the homemade bomb thrown at OpenAI CEO Sam Altman’s home.

I’d argue that the most common feeling about AI is somatic: a low-grade hum of difficult-to-place anxiety that’s the result of loud people constantly suggesting that the near future will look very little like the present and that nothing—your job or the social contract—might survive the transition.

The AI industry’s own apocalyptic messaging is feeding into this feeling. Even when AI executives urge for a deescalation in AI rhetoric, as Altman did in a recent blog post after the attacks, the language is grave. “The fear and anxiety about AI is justified,” he wrote. “We are in the process of witnessing the largest change to society in a long time, and perhaps ever.” A similar dynamic was at play in the rollout of Anthropic’s Mythos, a new model that the company claimed was so powerful that Anthropic could not release it widely because of concerns that it would lead to a global cybersecurity crisis. Should you be impressed, terrified, excited at the thought that the internet as we know it might no longer work? (Anthropic, of course, has a history of AI doomerism and a clear financial interest in making its products look historically powerful.)

As the industry has warned about AI’s risks, it has also done a remarkably poor job of articulating the positive vision of the future it wants to build. Attempts have been so grand as to come off as wildly patronizing. In April, OpenAI published a 13-page blueprint on “Industrial Policy for the Intelligence Age” with the quaint subheading: “Ideas to Keep People First.” Perhaps the most thoughtful (or at least the longest) articulation of what AI can do for good, a 14,000-word essay by Anthropic CEO Dario Amodei titled “Machines of Loving Grace,” is more of a wish list than a plan. And even at its most sincere, Amodei’s vision still comes off as alienating, even dystopian. Near the end of the piece, Amodei imagines a scenario in which AI has rendered the current economic system irrelevant. One solution, he muses, might be to create a new system in which economic decisions, including the allocation of resources, are off-loaded entirely to AI. He then nods to “a need for a broader societal conversation about how the economy should be organized.” Left unanswered is who gets to participate in that conversation. On X, the writer Noah Smith posed the question more bluntly: “In 20 or 50 years, will the heads of AI companies be de facto emperors of the world?”

Everything is flooding in faster than most people can process. Last week, Jack Clark, a co-founder of Anthropic, posted on X that he now believes that there’s a 60 percent chance that, by the end of 2028, “AI systems might soon be capable of building themselves.” AI CEOs have made many erroneous predictions about superintelligence, so should any of us really believe that a version of the singularity is 18 months away? What is a person to do with this information? Buy stock? Buy guns? Probably not learn to code. Here we are in 2026, living in a time when the insiders are girding themselves for a moment when the entire world becomes a computer, while many others are worried about gas prices and just trying to get through the day.

About the only thing clear in this moment is that a power struggle over who gets to define the coming years is looming. It is a struggle between the AI labs and between nations. The White House has intimated that it may very well be a struggle between the government and Silicon Valley. Silicon Valley AI lobbying spend suggests the same. But for most of us, navigating the jagged frontier will feel personal. What may seem like a civilizational imperative or seven-dimensional war-gaming to AI CEOs will seem to others like little more than Silicon Valley giving their boss a compelling reason to lay them or their loved ones off.

For the past decade, popular technology platforms—many of them built or championed by the same cohort who are building today’s AI tools—have favored acceleration over consideration. They incentivized us to operate by this same logic, often as the worst and loudest versions of ourselves. Over time, these tools flattened our arguments, our politics, our culture, compressing them into the same endless fights, such that people became ensconced in their own bespoke realities.

The same dynamics govern the AI conversation. The AI boom is a race, a gold rush, and the chasm between AI’s true believers and the malaised masses is getting wider. In the same feed, you can read a blind item about AI researchers taking up smoking because they believe that AI is going to cure lung cancer and a reported dispatch on “the shared feeling of being harvested by the future” taking hold in the United States and China. Silicon Valley’s leaders pay lip service to a societal conversation about what comes next, but their actions say something else: Keep up or be left behind. Humanity rewriting the social contract together sounds nice; less so when you have a gun to your head. Time is of the essence, we’re told. Maybe that’s true. But how can we build a future if we can’t agree on the present? A cynic might conclude that our input isn’t desired at all.

The planet’s biggest sporting event, the World Cup final, will take place this summer in MetLife Stadium, which is presently known as New York New Jersey Stadium because FIFA has strict rules on corporate branding. The stadium—whatever you want to call it—is located in the marshlands of New Jersey, about nine miles from Midtown Manhattan. On the day of the final, as on the dates of seven other matches throughout the World Cup tournament, an estimated 80,000 fans will converge at its gates.

But how will they get there? Some will drive, even though they’ll have to pay $225 to use one of the 5,000 available parking spots at a nearby shopping mall that is connected to the stadium area by pedestrian bridges. Others will buy a seat on a shuttle bus—originally $80, cut to $20 after last-minute maneuvering by New York Governor Kathy Hochul. (Some of these will be yellow school buses.) Or they will cough up whatever amount ride-share apps are charging on those days. And the rest—up to 40,000 people for each event—will take their chances on an infrequently used branch of New Jersey Transit that has struggled with large crowds in the past.

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In the coming months, America’s patchwork railway system will be similarly challenged—and its weaknesses exposed—across all 11 U.S. sites of World Cup matches. In Dallas, most people who are going to the stadium will either have to pay for expensive parking or take a commuter rail to a charter bus. Kansas City will rely entirely on charter buses. Where direct rail access is available, the trains aren’t likely to be convenient, and tickets may be outrageously expensive. New Jersey is a case in point: Last month, NJ Transit announced plans to charge $150 for each round-trip journey on a route that would otherwise cost less than $13.

That price was later reduced to $105, thanks to donations from various unnamed companies, then reduced again to $98 just before tickets went on sale—but the fact of any of these fares suggests a deeper problem. NJ Transit President and CEO Kris Kolluri explained the dismal math behind this pricing at a press conference in April, alluding to the agency’s enormous debt and degrading equipment. To transport all of those people to the stadium, he said, the agency would need to spend about $6 million a game, mostly for labor and security, as well as for maintenance work on 50 railcars; this would include the purchase of new wheels, axles, and air-conditioning units “to make sure that we don’t have the challenges we typically do.” Such costs could be passed on to New Jersey taxpayers, Kolluri pointed out, but “no one that I have spoken to thinks that that’s (1) fair and (2) reasonable.” So instead, the agency has done some simple arithmetic: $6 million in operating expenses divided by 40,000 riders equals $150.

From the start, the situation has had all the makings of a political brouhaha. When FIFA complained that the fare was too expensive, New Jersey Governor Mikie Sherrill argued that the association, which stands to bring in $11 billion in revenue from the tournament, should subsidize or cover the fares itself. A FIFA official shot back that the hiked-up fares would “diminish the economic benefit and lasting legacy the entire region stands to gain from hosting the World Cup.” Then the New York Post’s editorial board took issue with NJ Transit’s plan to close off its section of Manhattan’s Penn Station for long stretches on match days, arguing that the agency was “dissing” its regular riders. Separately, Pennsylvania Governor Josh Shapiro boasted that fans could get to and from the matches held in Philadelphia using the region’s SEPTA rail system for just $2.90.

Kolluri said that NJ Transit’s special challenges justify the (much, much) higher fare. The Philadelphia stadium is in the city, for example, and SEPTA trains already go there every day. MetLife Stadium, however, has no regular train service. It “is a suburban stadium,” he said, which is “very different fundamentally.” Isn’t that the problem, though? Europeans have lately been wondering on social media why this stadium was constructed where it is in the first place—stranded miles from the city center and encircled by highways, parking lots and swamps—and nobody has been able to supply them with a good answer. It’s just how we like it!

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One reporter asked Kolluri about the 2014 Super Bowl, held in the same location, also with approximately 80,000 people in attendance. NJ Transit did not raise fares anywhere near as much for that game, he pointed out. “First of all, do you know what happened in the Super Bowl?” Kolluri snapped. “I think you’re the only guy who may not know what actually happened.” What happened was widely reported travel chaos: Long lines and delays, and at one point, a request that people stay inside the stadium until some portion of the crowd dispersed from the train platform. The event went so poorly that the agency commissioned an independent investigation of its failures. Kolluri described all of this as having caused “PTSD,” and said that the situation was a reason to do things very differently this year. “People think about that moment and say we can never let that happen again,” he said. (People did, in fact, let that happen again in 2019, when thousands of fans got stuck waiting for hours in the darkness for a NJ Transit ride after a WrestleMania event.)

The $150-a-ticket pricing, Kolluri argued, was only what would be needed to prevent catastrophe. “I think that’s a defensible claim,” says Zoe Baldwin, the vice president of state programs at the Regional Plan Association, a nonprofit focused on economic development and quality of life in New York, New Jersey, and Connecticut. “We have a very old system that is in desperate need of overhaul, let alone maintenance.” Equipment failures are more common in the summer, she told me, so NJ Transit will have to spend on back-up crews and engines in case any trains are taken out of service. She seemed optimistic about the agency’s ability to handle the tournament crowds, and she emphasized that the trip out to the stadium would be a great opportunity for people all over the world to get a look at one of the country’s biggest and most fascinating urban wetlands. When I asked her whether those same people might be horrified by the look they get at New Jersey’s tangle of unwalkable roadways and parking lots, she protested: “What are they going to think when they go to L.A., then?”

To her point, the most public drama over World Cup transportation until now has occurred in a region that has better public-transit options than any other part of the United States does. The railway infrastructure throughout the Northeast may be old and shoddy—for example, Amtrak service between New York and Boston was recently suspended because pieces of a highway on-ramp had fallen onto the tracks—but at least it exists. Just two World Cup host cities in the U.S.—Seattle and San Francisco—have an Amtrak station anywhere near their stadium. In Houston, where fans can take the city’s light-rail system, two of the relevant lines run only once every 12 minutes. In Los Angeles, the matches will be accessible via shuttle-bus service from designated Metro drop-off points. Even back East in Philadelphia, where SEPTA service goes directly to the stadium, the system will be strained: A spokesperson estimated that that line can transport 15,000 people an hour, but twice that many are expected to take a train to each match.

When I asked Jim Mathews, the president and CEO of the nonprofit Rail Passengers Association, about his impressions of the various host cities’ transportation plans, he complimented the Los Angeles strategy on the grounds that it would be affordable and temporarily link several independent transit systems. But he did not agree with the triple-digit price tag for NJ Transit rides, or the $80 fares for those who take a train from Boston to a match at Gillette Stadium. “You’re taking this moment when the spotlight of the world is on you, and you’re making it stupidly expensive,” he told me. “It just shows you what happens when you go for decades underinvesting in capacity.”

Mathews said he’s worried that visitors from overseas will be shocked when they arrive in the U.S. and get a look at its trains. Although some cities here now have more transit options than they did a few years ago, tourists may still be disappointed by the scarcity of options. And despite Americans’ dramatic increase in interest in soccer over the past three decades, he expected we’d be embarrassed on the field too: “We are still going to exit in the first round.”