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The first thing I ever said to my dog was, “Do you want to come home with me?” He was six pounds, and 10 weeks old. He craned his head forward and sniffed my mouth.

In the four years since, I have continued to pepper him with questions that he cannot answer. I ask him what he’s up to, if he wants to go for a walk, if he’s feeling sleepy. When he is sick, I ask him what is wrong; when another dog growls at him, I pull him aside to ask if he’s okay. He does what he can to relay his thoughts back to me: He barks; he sighs; he scratches at the door.

But of course we have never talked to each other, not really. Some 15,000 years since humans first domesticated the wolf, scientists have learned that different barks mean different things—for instance, dogs use lower, longer barks for strangers—but our understanding of dog communication remains rather limited. (Researchers are careful to call it communication, not language, because no animal has been shown to possess the same complexity of verbal systems as humans.)

Although a bark at a squirrel is easy enough to decipher (I will eat you!), humans have more trouble knowing whether a whine is just a dog having random feelings on a Tuesday—or something far more serious. Dog owners often joke about how they’d give up years of their life just to have a chance to talk to their pet for a single hour or day. Meanwhile, hucksters posing as dog whisperers and pet psychics have happily taken their cash by claiming to be able to help them translate their dogs’ inner thoughts.

Now, amid a wave of broader interest in applications for artificial intelligence, some dog researchers are hoping that AI might provide answers. In theory, the technology is well suited for such a purpose. AI, at its core, is a pattern-recognition machine. ChatGPT is able to respond in language that seems human, because it has been trained on massive datasets of writing, which it then mimics in its responses. A similar premise applies to other generative-AI programs; large language models identify patterns in the data they’re fed, map relationships among them, and produce outputs accordingly.

Researchers are working with this same theory when it comes to dogs. They’re feeding audio or video of canines to a model, alongside text descriptions of what the dogs are doing. Then they’re seeing if the model can identify statistical patterns between the animals’ observed behavior and the noises they’re making. In effect, they’re attempting to “translate” barks.

Researchers have used similar approaches to study dog communication since at least 2006, but AI has recently gotten far better at processing huge amounts of data. Don’t expect to discuss the philosophy of Immanuel Kant with Fido over coffee anytime soon, however. It’s still early days, and researchers don’t know what kind of breakthroughs AI could deliver—if any at all. “It’s got huge potential—but the gap between the potential and the actuality hasn’t quite emerged yet,” Vanessa Woods, a dog-cognition expert at Duke University, told me.

Right now, researchers have a big problem: data. Modern chatbots are trained on large collections of text—trillions of words—that give them the illusion of language fluency. To create a model capable of translating, say, dog barks into English (if such a thing is even possible), researchers would need millions, if not billions, of neatly cataloged clips. These barks will need to be thoroughly labeled by age, breed, and situation—separating out a 10-year-old male labradoodle barking at a stranger from a six-week-old bichon frise puppy playing with its littermate.

No such catalog currently exists. This is one of the great ironies of the project: Dogs are all around us, constantly captured by phones and doorbell cameras and CCTV. You don’t need to watch Planet Earth to see the canine living in its natural habitat; the internet is filled with more clips of dogs than anyone could watch in a lifetime. And yet all of this media has never been cataloged in a serious way, at least not on the scale that would be necessary for us to better understand what their barks mean.

Perhaps the best catalog that exists is from researchers in Mexico, who have systematically recorded dogs in their homes in specific situations, getting them to bark by, say, knocking on a door or squeaking a favorite toy. A research team at the University of Michigan took some of the 20,000 recordings included in the dataset and fed it into a model trained to recognize human speech. They played barks for the model, and then had it predict what they were barking at, just based on sound. The model could predict which situation preceded the bark with about 60 percent accuracy. That’s nowhere near perfect, but still better than chance, especially considering that the model had more than a dozen bark contexts to pick from.

The same approach of using AI to decipher dog barks is happening with other animals. Perhaps the most promising work is with whale chatter, as my colleague Ross Andersen has written. Other researchers are tackling pigs, bats, chimpanzees, and dolphins. One foundation is offering up to $10 million in prize money to anyone who can “crack the code” and have a two-way conversation with an animal using generative AI.

[Read: How first contact with whale civilization could unfold]

Dogs probably won’t be the animals that help scientists win the prize. “I do not think they necessarily use words and sentences and paragraphs,” Rada Mihalcea, a co-author of the Michigan study, told me over Zoom. (Naturally, in the middle of our call, a stranger knocked on my door, causing my foster dog to bark.) As much as dog owners like myself might want something akin to Google Translate for dogs, Mihalcea’s starting with much more narrow ambitions. She hopes this line of research can “help us get an understanding into what is even there as a linguistic system—if there is such a system.”  

Another research group, led by Kenny Zhu at the University of Texas at Arlington, is taking a different approach. His team is scraping massive amounts of dog videos from YouTube. But the data are extremely noisy—quite literally. The researchers have to isolate the barks from all the other sounds that happen in the background of the videos, which makes the process onerous. Zhu’s team does have preliminary findings: They had their algorithms process the sounds of six different breeds (huskies, Shiba Inus, pit bulls, German shepherds, Labradors, and Chihuahuas), and believe they’ve found 105 unique phonemes, or sound units, that span all the breeds.

Even if researchers are able to eventually get a perfect dataset, they’ll run into another problem: There’s no way to know for sure that whatever observations the AI makes is right. When training other AI models on human languages, a native speaker can verify that an output is correct, and help fine-tune the model. No dog will ever be able to verify the AI’s results. (Imagine a dog sitting in an academic research lab, nodding solemnly: Yes, that’s correct.“Ruff-ruff-ruff” means“Give me the chicken.”) The dream of AI as an intermediary between humans and dogs faces a fundamental bias: It is human researchers who are using human-made AI models and human ideas of language to better understand canines. No matter how good the technology gets, there will always be unknowns.

The focus on better understanding dogs’ verbal noises can obscure how much we already know about them. Dogs have evolved to better communicate with humans: Their barks have changed, and their eyes have grown more expressive. Feral dogs and wolves bark less than pets, suggesting that humans are a big reason why our pups make noise. “The whole thing about dog genius is that they can communicate with us without speaking,” Woods told me. “We can also read them really clearly, which is why we’re so in love with them.”

[Read: Dogs are entering a new wave of domestication]

I know what she means. During a heat wave this summer, I decided to buy heat-resistant dog boots to protect my pup from the scorching pavement. You put them on by stretching them over your dog’s paws, and snapping them into place. The first time I put them on my dog, he stared at me. When I tried to walk him in them later that week, he thrashed in the grass and ran around chaotically. He did not want to wear the boots. And I did not need an AI to know that.

Over the past few months, some of the most prominent people in AI have fashioned themselves as modern messiahs and their products as deities. Top executives and respected researchers at the world’s biggest tech companies, including a recent Nobel laureate, are all at once insisting that superintelligent software is just around the corner, going so far as to provide timelines: They will build it within six years, or four years, or maybe just two.

Although AI executives commonly speak of the coming AGI revolution—referring to artificial “general” intelligence that rivals or exceeds human capability—they notably have all at this moment coalesced around real, albeit loose, deadlines. Many of their prophecies also have an undeniable utopian slant. First, Demis Hassabis, the head of Google DeepMind, repeated in August his suggestion from earlier this year that AGI could arrive by 2030, adding that “we could cure most diseases within the next decade or two.” A month later, even Meta’s more typically grounded chief AI scientist, Yann LeCun, said he expected powerful and all-knowing AI assistants within years, or perhaps a decade. Then the CEO of OpenAI, Sam Altman, wrote a blog post stating that “it is possible that we will have superintelligence in a few thousand days,” which would in turn make such dreams as “fixing the climate” and “establishing a space colony” reality. Not to be outdone, Dario Amodei, the chief executive of the rival AI start-up Anthropic, wrote in a sprawling self-published essay last week that such ultra-powerful AI “could come as early as 2026.” He predicts that the technology will end disease and poverty and bring about “a renaissance of liberal democracy and human rights,” and that “many will be literally moved to tears” as they behold these accomplishments. The tech, he writes, is “a thing of transcendent beauty.”

These are four of the most significant and well respected figures in the AI boom; at least in theory, they know what they’re talking about—much more so than, say, Elon Musk, who has predicted superhuman AI by the end of 2025. Altman’s start-up has been leading the AI race since even before the launch of ChatGPT, and Amodei has co-authored several of the papers underlying today’s generative AI. Google DeepMind created AI programs that mastered chess and Go and then “solved” protein folding—a transformative moment for drug discovery that won Hassabis a Nobel Prize in chemistry last week. LeCun is considered one of the “godfathers of AI.”

Perhaps all four executives are aware of top-secret research that prompted their words. Certainly, their predictions are couched in somewhat-scientific language about “deep learning” and “scaling.” But the public has not seen any eureka moments of late. Even OpenAI’s new “reasoning models,” which the start-up claims can “think” like humans and solve Ph.D.-level science problems, remain unproven, still in a preview stage and with plenty of skeptics.

[Read: It’s time to stop taking Sam Altman at his word]

Perhaps this new and newly bullish wave of forecasts doesn’t actually imply a surge of confidence but just the opposite. These grand pronouncements are being made at the same time that a flurry of industry news has been clarifying AI’s historically immense energy and capital requirements. Generative-AI models are far larger and more complex than traditional software, and the corresponding data centers require land, very expensive computer chips, and huge amounts of power to build, run, and cool. Right now, there simply isn’t enough electricity available, and data-center power demands are already straining grids around the world. Anticipating further growth, old fossil-fuel plants are staying online for longer; in the past month alone, Microsoft, Google, and Amazon have all signed contracts to purchase electricity from or support the building of nuclear power plants.

All of this infrastructure will be extraordinarily expensive, requiring perhaps trillions of dollars of investment in the next few years. Over the summer, The Information reported that Anthropic expects to lose nearly $3 billion this year. And last month, the same outlet reported that OpenAI projects that its losses could nearly triple to $14 billion in 2026 and that it will lose money until 2029, when, it claims, revenue will reach $100 billion (and by which time the miraculous AGI may have arrived). Microsoft and Google are spending more than $10 billion every few months on data centers and AI infrastructure. Exactly how the technology warrants such spending—which is on the scale of, and may soon dwarf, that of the Apollo missions and the interstate-highway system—is entirely unclear, and investors are taking notice.

When Microsoft reported its most recent earnings, its cloud-computing business, which includes many of its AI offerings, had grown by 29 percent—but the company’s stock had still tanked because it hadn’t met expectations. Google actually topped its overall ad-revenue expectations in its latest earnings, but its shares also fell afterward because the growth wasn’t enough to match the company’s absurd spending on AI. Even Nvidia, which has used its advanced AI hardware to become the second-largest company in the world, experienced a stock dip in August despite reporting 122 percent revenue growth: Such eye-catching numbers may just not have been high enough for investors who have been promised nothing short of AGI.

[Read: Silicon Valley’s trillion-dollar leap of faith]

Absent a solid, self-sustaining business model, all that the generative-AI industry has to run on is faith. Both costs and expectations are so high that no product or amount of revenue, in the near term, can sustain them—but raising the stakes could. Promises of superintelligence help justify further, unprecedented spending. Indeed, Nvidia’s chief executive, Jensen Huang, said this month that AGI assistants will come “soon, in some form,” and he has previously predicted that AI will surpass humans on many cognitive tests in five years. Amodei’s and Hassabis’s visions that omniscient computer programs will soon end all disease is worth any amount of spending today. With such tight competition among the top AI firms, if a rival executive makes a grand claim, there is pressure to reciprocate.

Altman, Amodei, Hassabis, and other tech executives are fond of lauding the so-called AI scaling laws, referencing the belief that feeding AI programs more data, more computer chips, and more electricity will make them better. What that really entails, of course, is pumping their chatbots with more money—which means that enormous expenditures, absurd projected energy demands, and high losses might really be a badge of honor. In this tautology, the act of spending is proof that the spending is justified.

More important than any algorithmic scaling law, then, might be a rhetorical scaling law: bold prediction leading to lavish investment that requires a still-more-outlandish prediction, and so on. Only two years ago, Blake Lemoine, a Google engineer, was ridiculed for suggesting that a Google AI model was sentient. Today, the company’s top brass are on the verge of saying the same.

All of this financial and technological speculation has, however, created something a bit more solid: self-imposed deadlines. In 2026, 2030, or a few thousand days, it will be time to check in with all the AI messiahs. Generative AI—boom or bubble—finally has an expiration date.

This article contains spoilers for Playground.

From paintings on ancient cave walls to parables, fables, and memes, animals have served as important storytelling tools. For instance, in Saul Bellow’s novel Humboldt’s Gift, the narrator describes the novel’s title character, a fearsome, mercurial poet, by observing, “A surfaced whale beside your boat might look at you as he looked with his wide-set gray eyes.” This deceptively simple metaphor challenges us to imagine an unsettling encounter with a big, strange presence, and situates us in a literary tradition with its sly allusion to Herman Melville’s Moby-Dick. Bellow’s simile is instructive insofar as it’s evocative, and appealingly demanding in its layers of meaning compressed into a single sentence.

Contrast this with an artist’s encounter, in Richard Powers’s new novel, Playground, with a juvenile albatross: “Ina reached her hand into the chest of the decomposing bird and drew out two bottle caps, a squirt top, the bottom of a black film canister at least fifteen years old, a disposable cigarette lighter, a few meters of tangled-up monofilament line, and a button in the shape of a daisy.”

Like many passages in Playground, this teaches me important and timely things in serviceable prose: Plastics are bad because they kill birds; the consequences of human despoilment are gruesome if we dare look closely enough. I get it, I get it all too easily, when I read Powers’s preachier novels, whether they’re about trees (The Overstory), race (The Time of Our Singing), refugees (Generosity), or the challenges of single fatherhood for astrobiologists (Bewilderment). Powers has also written novels of greater subtlety, driven by a purist’s fascination with the inner workings of complex systems and instruments: The Gold Bug Variations, Galatea 2.2, Orfeo, Plowing the Dark.

Playground seems on the surface to belong in the first group—the flat-character morality plays that have come to define Powers’s later career. It extends and deepens his ongoing project of telling stories that combine lyrical mastery with environmentalist didacticism to criticize humankind’s treatment of the world while attending to the promise of the nonhuman—natural and artificial. The setup of his latest also addresses the reader’s dilemma in confronting such work: What, exactly, is the contemporary novel for? To teach, or to challenge? Fortunately, for those who stick with Playground to the end, Powers doesn’t answer the question in flattering ways but instead complicates it confidently, exploring what art might look like in a less human future.

[Read: Writing the Pulitzer-winning The Overstory changed Richard Powers’s life]

The new novel begins with a creation story cum prayer about Ta’aroa, the Indigenous Polynesian creator god, before proceeding to detail the dilemmas of people living on the remote South Pacific island Makatea. During the early years of the 20th century, when the island was rich in phosphate crucial to industrial agriculture, it was ruined by the extraction work of foreign companies. It’s finally recovering when its 82 remaining human inhabitants are approached by a conglomerate seeking a host to build components of future floating cities. These new artificial islands will serve as swanky sanctuaries for uber-elites hoping to ride out the collapse of human society (or maybe just escape state regulations).

The novel’s human drama plays out as the islanders determine whether to vote yes to the seasteading project, and focuses on the intertwined stories of four main characters. Ina, a half-Polynesian sculptor and an attentive mom, and her husband, Rafi, a literary-minded Black educator, both live on Makatea. Todd, who is white, is Rafi’s boyhood friend turned enemy: They first bonded as bookish Chicago kids from dysfunctional families who loved playing Go. Todd is now the billionaire founder of a world-beating, all-in-one social-media, gaming, and commerce platform called Playground—and also the discreet money behind the seasteading endeavor. The project is in many ways motivated by Todd’s lifelong love of the ocean, itself inspired by the novel’s fourth major character, Evie, a trailblazing French Canadian scuba diver and scientist who holds Jane Goodall–grade celebrity status.

Playground is told in two ways that feel by turns overlong and undercooked—until they add up to something unexpected and genuinely fascinating. The dominant thread suggests a seemingly conventional, multi-perspective third-person novel featuring braided backstories interspersed with a chronicle of deliberations among the atoll’s inhabitants about whether to approve the project. Another narrative runs in tandem: a first-person series of reflections on Todd’s life and work, provoked and also marred—in their undulance and ellipsism—by his diagnosis of the degenerative brain disease known as Lewy body dementia. “I’m suffering from what we computer folks call latency,” Todd observes early on. “Retreating into the past … as more recent months and years grow fuzzy.” Three emotions recur for Todd: his regret over the break with Rafi and (by extension) Ina, the only people he’s ever felt close to; his longing, as a Midwestern boy, for the ocean wonders he first read about in Evie’s best-selling book; and his self-satisfaction as a Big Tech visionary. (“I bent under the obligation to become the first person to reach the Future. And here I am, successful at last.”)

Where is Todd, exactly? Ostensibly, he’s living in a splendid isolation afforded by his extreme wealth while his slick minions press the people of Makatea to agree to the conglomerate’s offer of large-scale economic renewal—while also implying that they could just as easily ask inhabitants of another Pacific island. On Makatea, Rafi broods and minds the children, at least until he becomes very upset after learning about Todd’s involvement in the project; Ina makes a dramatic protest sculpture out of garbage, some of it found in the bodies of little birds; the other islanders debate their voting rights versus the rights of the surrounding marine life; and Evie, now in her 90s, visits like an ethereal, demure white sage, commanding credibility from the islanders because of her mystical relationship to sea creatures, which we hear about again and again (and again). Eventually, nearing total mental and physical breakdown, Todd makes a dramatic trip to Makatea to meet Rafi and Ina and Evie, in hopes of achieving a perfect confluence of his goals in the realms of business, relationships, and world-building.

[Read: Going to extremes]

If this all sounds like fantasy fiction for rich white people, that’s because it is. I’m not being a crank here, whining again about how Powers falls short of the great American masters of marine-life metaphors. I’m pointing, in fact, to a revelation near the very end of the novel, which discloses its stunning conceit. The spoiler is warranted here, because revealing Powers’s destination serves his potential readers by placing fewer demands on their patience than he does. Todd is in fact immersed in an AI-generated story, created by a successor version of his first major creation. In the first-person sections, set off in italics, he’s been speaking to the AI, not us, the whole time. He’s fed it as much material as he could, from memory and information, about Makatea and about himself, Rafi, Ina, and Evie. In turn, the AI has created for him the very story we’ve been reading, interleaved with his reflections. His success lies in crafting and reading an artificially constructed story that fulfills wishes, answers unmet needs, and resolves regrets that ring his actual life.

The AI-generated components are incoherent, clichéd, cloying, and condescending: confusing chronologies, stereotypes about the simple nobility of Makatea’s Indigenous people and the resilience of inner-city Chicagoans, the sacral grandeur of animal and technological sentients. But suddenly, all of that makes sense, because the author has constructed a Powersian hall of mirrors: a novel that imagines what a novel might look like if it were composed by an AI developed by a misanthropic genius loner.

Ingenious tricks and clever devices abound in Powers’s fiction, but never before with the provocative implications of the turn in Playground. The novel offers a superb reversal of the scaled-up scavenging normative to AI art, but more than that, Playground challenges the readership—both admirers and critics—that Powers’s past work has created. Will true believers in Powers’s literary-ethical divinity feel betrayed by the late revelation, given their sincere investment in the story? Will they not only be moved by a fairy tale machined for a big, bad tech guy, the toughest of antiheroes to side with, but also believe that a nonhuman intelligence can capably capture and compel their imaginations? I hope so, inasmuch as this would lead to an intellectual reckoning of a different order than the surface expectation—that Powers’s latest novel simply teaches us that plastics are bad and Pacific Islanders are good. Moreover, reading Powers in this more difficult, demanding way affirms the imperative that Literature—recalcitrant in its ideas, characters, and storylines—should invite and sustain more of and from its readers.

In the end, Playground is exactly what I’d presumed it wasn’t: difficult, ambiguous, and resistant to au courant notions, all while trafficking in such ideas with deceptive coolness and ease. The novel exposes our dependency on fiction that promises morally clear accounts of our right-ordered relationship to animals, nature, each other, technology, literature—and to story itself.

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