When a whale dies, it often decomposes quite quickly—the process starts within hours of an animal’s stranding on shore. Depending on the species, they may have six inches or more of blubber, an insulating layer that traps heat inside and turns their internal organs to mush.  That can make Jennifer Bloodgood’s job very difficult. As a wildlife veterinarian with New York state and Cornell Wildlife Health Lab, she’s an expert on conducting whale necropsies, as autopsies on animals are called—and she knows that it’s best to start them quickly, or she could miss key clues about how the gigantic mammal perished. These investigations have become especially important because whale deaths have become a political flashpoint with significant consequences. There are currently three active mortality events for whales in the Atlantic, meaning clusters of deaths that experts consider unusual. And Republican lawmakers, influential conservative think tanks, and—most notably—President Donald Trump (a longtime enemy of wind power) are making dubious claims about how offshore wind farms are responsible. This story is part of MIT Technology Review’s series “The New Conspiracy Age,” on how the present boom in conspiracy theories is reshaping science and technology. This has become the basis for a nebulous quasi-conspiracy theory, in which some anti-wind groups have claimed that the surveying technology used to map sites for wind farms can disturb the animals. Another argument is that the noise emitted by operational turbines disrupts whales’ communication and navigation. “The windmills are driving the whales crazy, obviously,” President Trump remarked in January.  But any finger-pointing at wind turbines for whale deaths ignores the fact that whales have been washing up on beaches since long before the giant machines were rooted in the ocean floor. This is something that has always happened. And the scientific consensus is clear: There’s no evidence that wind farms are the cause of recent increases in whale deaths. 
There’s still a lot that researchers don’t know about whales’ lives and deaths, but experts often conduct dozens of in-depth (and somewhat gruesome) investigations each year on the US East Coast alone. And in the active mortality events there, the data shows that humpback whales and North Atlantic right whales are typically casualties of human interaction, falling victim to things like boat strikes and entanglement in fishing gear. (In fact, in Bloodgood’s experience, about half the humpback whales that are in good enough condition to necropsy show signs of a vessel strike or other human interaction.) And minke whales appear to be falling to a common infection called brucella, which she’s also observed.  “When a whale strands, there’s a huge effort that goes into responding and figuring out why it died,” Bloodgood says. “Many people’s job is to go out and figure out what’s happening.”
And, notably, what they’re finding is not death by turbine. “There is currently no evidence,” she tells me, “that wind energy is influencing whale strandings.” Bloodgood is largely clinical as she talks about her work, describing the sometimes gory work of necropsies with a straight face from her simply decorated office—though on several occasions she chuckles and apologizes after sharing a particularly graphic detail.  “We can learn so much from dead animals,” she tells me. By investigating bodies that wash up on shore, she and her fellow experts can uncover basic details like their species and age, but also what they ate, and, of course, why they died. They may look for signs of disease, or for evidence of human interference—boats, fishing nets, and yes, wind farm development.  The first step after someone spots a whale washed up on shore is to call local authorities and groups of scientists, veterinarians, and volunteers, called stranding networks, that can help rescue, rehabilitate, and release the ones that are still alive—or perform necropsies on the ones that aren’t.  Over the past few years, Bloodgood has helped with nine strandings across New Jersey, New York, and Delaware. (As a professor, she often brings students along. She’s had to introduce a lottery system since so many are interested.) How any necropsy unfolds depends on the condition of the whale and on its stranding location. But generally, if there’s a fresh enough body to justify a full necropsy, a large team will get together on the scene. They’ll start with an external exam, looking for anything unusual on the skin, eyes, blowhole, and mouth. Then they’ll systematically dismantle the whale, noting anything that seems abnormal, and taking samples to send back to the lab. 
When the researchers are evaluating a cause of death, they’re looking at the whole picture, trying to find the most likely cause and gather evidence to discount all other potential causes. There’s not always a smoking gun, Bloodgood says. But a thorough enough examination can usually yield some meaningful clues.  Say, for instance, a whale’s suspected cause of death is a boat strike. Researchers will look out for bruises and cuts during the external examination, and then they’ll try to spot broken bones and internal hemorrhaging. But they’ll also keep their eyes out for other issues, like lesions that can signal brucellosis.  Usually, you want experienced cutters to do the carving, Bloodgood says, since whales are so large it’s usually necessary to use knives that are one or two feet long. Whales are also oily, so the knives can get quite slippery.   After cutting through the thick blubber layer, researchers may use gaff hooks to spear skin or organs in order to move them around or keep them out of the way. There’s no rigid order of operations, though they’ll typically look at the major organs including lungs, liver, kidneys, and brain; it’s also usually helpful to open up the digestive system to see what the whale has been eating, which Bloodgood says increasingly includes plastic.
Accessing all the necessary organs can require moving the whale. Sometimes, if a stranding location is accessible enough, heavy equipment like an excavator can help lift part of the body to assist in splitting it open. When that’s not the case, experts can use what’s called the window method, Bloodgood says. That basically just means cutting strategically placed holes along the body to access the desired organs. Near the pectoral fin is generally a good target for a sample of the lung, for example. One problem with this method is that it doesn’t always work if the body has decomposed and been tossed around in the waves before washing onshore. In that case, things get all jumbled up, and the lungs could end up by the tail, for instance. After the deconstructing is done, Bloodgood goes back to the lab, taking samples of each of the tissues to conduct further analysis. One area of interest for her is ear bones. If a whale were in fact affected by the sound waves used by boats surveying wind farm locations (something that’s unlikely, given the type of sound waves used), their ear bones might show evidence of trauma associated with noise. That damage could be visible under a microscope or in a CT scan.  Bloodgood has been investigating this theory, with a particular focus on dolphins—their heads, unlike whales’, are small enough to fit in the scanner. There’s been no sign of such damage in any of the samples she’s examined.  
Despite all the things that experts like Bloodgood can observe and test for, the system can never be perfect. Not all dead whales end up on beaches, and not all that do are in good enough shape for a thorough investigation. What’s more, it turns out to be quite difficult to entirely disprove that any single cause contributed to a whale’s death. Even if a stranded animal had an infection, or was hit by a boat, it’s theoretically possible there was another factor as well. Still, in many cases, the necropsy turns up enough for scientists to feel confident assigning a cause of death. And after an investigation is complete, they publish a report, which is then analyzed by the National Oceanic and Atmospheric Administration. Enough such reports can point to trends.   But most people almost certainly don’t see those reports or seek out that data. Even as whale deaths have become a heated point of debate at the highest levels of politics, Bloodgood feels the public doesn’t always recognize the care researchers take to investigate what’s going on. “I think a lot of people don’t realize the amount of effort that goes into understanding why whales die,” she says.  She notes that these experts are working with limited, and dwindling, public support and resources. The Trump administration recently canceled funding for two programs that used aerial surveys and underwater listening devices to track whale populations and better understand the effects of human activity—including offshore wind development.  At the same time, there’s more pressure, and more misinformation out there, she adds:  “I think it’s just become increasingly important to be transparent with the public.” In addition to publishing reports from necropsies online, some stranding networks give updates to local communities on social media, too.  “If you don’t tell people what you find, they start coming up with their own ideas,” Bloodgood says. “If they think you’re hiding something, that’s the worst.” 

Are you feeling it? I hear it’s close: two years, five years—maybe next year! And I hear it’s going to change everything: it will cure disease, save the planet, and usher in an age of abundance. It will solve our biggest problems in ways we cannot yet imagine. It will redefine what it means to be human.  Wait—what if that’s all too good to be true? Because I also hear it will bring on the apocalypse and kill us all …  Either way, and whatever your timeline, something big is about to happen.  We could be talking about the Second Coming. Or the day when Heaven’s Gaters imagined they’d be picked up by a UFO and transformed into enlightened aliens. Or the moment when Donald Trump finally decides to deliver the storm that Q promised. But no. We’re of course talking about artificial general intelligence, or AGI—that hypothetical near-future technology that (I hear) will be able to do pretty much whatever a human brain can do. This story is part of MIT Technology Review’s series “The New Conspiracy Age,” on how the present boom in conspiracy theories is reshaping science and technology. For many, AGI is more than just a technology. In tech hubs like Silicon Valley, it’s talked about in mystical terms. Ilya Sutskever, cofounder and former chief scientist at OpenAI, is said to have led chants of “Feel the AGI!” at team meetings. And he feels it more than most: In 2024, he left OpenAI, whose stated mission is to ensure that AGI benefits all of humanity, to cofound Safe Superintelligence, a startup dedicated to figuring out how to avoid a so-called rogue AGI (or control it when it comes). Superintelligence is the hot new flavor—AGI but better!—introduced as talk of AGI becomes commonplace. Sutskever also exemplifies the mixed-up motivations at play among many self-anointed AGI evangelists. He has spent his career building the foundations for a future technology that he now finds terrifying. “It’s going to be monumental, earth-shattering—there will be a before and an after,” he told me a few months before he quit OpenAI. When I asked him why he had redirected his efforts into reining that technology in, he said: “I’m doing it for my own self-interest. It’s obviously important that any superintelligence anyone builds does not go rogue. Obviously.”
He’s far from alone in his grandiose, even apocalyptic, thinking.  Every age has its believers, people with an unshakeable faith that something huge is about to happen—a before and an after that they are privileged (or doomed) to live through.   For us, that’s the promised advent of AGI. People are used to hearing that this or that is the next big thing, says Shannon Vallor, who studies the ethics of technology at the University of Edinburgh. “It used to be the computer age and then it was the internet age and now it’s the AI age,” she says. “It’s normal to have something presented to you and be told that this thing is the future. What’s different, of course, is that in contrast to computers and the internet, AGI doesn’t exist.” And that’s why feeling the AGI is not the same as boosting the next big thing. There’s something weirder going on. Here’s what I think: AGI is a lot like a conspiracy theory, and it may be the most consequential one of our time.
I have been reporting on artificial intelligence for more than a decade, and I’ve watched the idea of AGI bubble up from the backwaters to become the dominant narrative shaping an entire industry. A onetime pipe dream now props up the profit lines of some of the world’s most valuable companies and thus, you could argue, the US stock market. It justifies dizzying down payments on the new power plants and data centers that we’re told are needed to make the dream come true. Fixated on this hypothetical technology, AI firms are selling us hard.  Just listen to what the heads of some of those companies are telling us. AGI will be as smart as an entire “country of geniuses” (Dario Amodei, CEO of Anthropic); it will kick-start “an era of maximum human flourishing, where we travel to the stars and colonize the galaxy” (Demis Hassabis, CEO of Google DeepMind); it will “massively increase abundance and prosperity,” even encourage people to enjoy life more and have more children (Sam Altman, CEO of OpenAI). That’s some product. Or not. Don’t forget the flip side, of course. When those people are not shilling for utopia, they’re saving us from hell. In 2023, Amodei, Hassabis, and Altman all put their names to a 22-word statement that read: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” Elon Musk says AI has a 20% chance of annihilating humans.  “I’ve noticed recently that superintelligence, which I thought was a concept you definitely shouldn’t mention if you want to be taken seriously in public, is being thrown around by tech CEOs who are apparently planning to build it,” says Katja Grace, lead researcher at AI Impacts, an organization that surveys AI researchers about their field. “I think it’s easy to feel like this is fine. They also say it’s going to kill us, but they’re laughing while they say it.” You have to admit it all sounds a bit tinfoil hat. If you’re building a conspiracy theory, you need a few things in the mix: a scheme that’s flexible enough to sustain belief even when things don’t work out as planned; the promise of a better future that can be realized only if believers uncover hidden truths; and a hope for salvation from the horrors of this world.  AGI just about checks all those boxes. The more you poke at the idea, the more it starts to look like a conspiracy. It’s not, of course—not exactly. And I’m not drawing this parallel to dismiss the very real, often jaw-dropping results achieved by many people in this field, including (or especially) the AGI believers.  But by zooming in on things that AGI has in common with genuine conspiracies, I think we can bring the whole concept into better focus and reveal it for what it is: a techno-utopian (or techno-dystopian—pick your pill) fever dream that got its hooks into some pretty deep-seated beliefs that have made it hard to shake. This isn’t just a provocative thought experiment. It’s important to question what we’re told about AGI because buying into the idea isn’t harmless. Right now, AGI is the most important narrative in tech—and, to some extent, in the global economy. We can’t make sense of what’s going on in AI without understanding where the idea of AGI came from, why it is so compelling, and how it shapes the way we think about technology overall. 

I get it, I get it—calling AGI a conspiracy isn’t a perfect analogy. It will also piss a lot of people off. But come with me down this rabbit hole and let me show you the light.  How Silicon Valley got AGI-pilled It had a ring to it A typical conspiracy theory usually starts out on the fringes. Maybe it’s just a couple of people posting on a message board, gathering “evidence.” Maybe it’s a few people out in the desert with binoculars waiting to spot some bright lights in the sky. But some conspiracy theories get lucky, if you will: They start to percolate more widely; they start to become a bit more acceptable; they start to influence people in power. Maybe it’s the UFOs (ahem, sorry, “unidentified aerial phenomena”) that are now formally and openly discussed in government hearings. Maybe it’s vaccine skepticism (yes, a much more dangerous example) that becomes official policy. And it’s impossible to ignore that artificial general intelligence has followed a pretty similar trajectory to its more overtly conspiratorial brethren.  Let’s go back to 2007, when AI wasn’t sexy and it wasn’t cool. Companies like Amazon and Netflix (which was still sending out DVDs in the mail) were using machine-learning models, proto-organisms to today’s LLM behemoths, to recommend movies and books to customers. But that was more or less it. Ben Goertzel had far bigger plans. About a decade earlier, the AI researcher had set up a dot-com startup called Webmind to train what he thought of as a kind of digital baby brain on the early internet. Childless, Webmind soon went bust. But Goertzel was an influential figure in a fringe community of researchers who had dreamed for years of building humanlike artificial intelligence, an all-purpose computer program that could do many of the things people can do (and do them better). It was a vision that went far beyond the kind of tech that Netflix was experimenting with. Goertzel wanted to put out a book promoting that vision, and he needed a name that would set it apart from the humdrum AI of the time. A former Webmind employee named Shane Legg suggested Artificial General Intelligence. It had a ring to it. A few years later, Legg cofounded DeepMind with Demis Hassabis and Mustafa Suleyman. But to most serious researchers at the time, the claim that AI would one day mimic human abilities was a bit of a joke. AGI used to be a dirty word, Sutskever told me. Andrew Ng, founder of Google Brain and former chief scientist at the Chinese tech giant Baidu, told me he thought it was loony. So what happened? I caught up with Goertzel last month to ask how a fringe idea went from crackpot to commonplace. “I’m sort of a complex chaotic systems guy, so I have a low estimate that I actually know what the nonlinear dynamic in the memosphere really was,” he said. (Translation: It’s complicated.) 
Goertzel reckons a few things took the idea mainstream. The first is the Conference on Artificial General Intelligence, an annual meeting of researchers that he helped set up in 2008, the year after his book was published. The conference was often coordinated with top mainstream academic meetups, such as the Association for the Advancement of Artificial Intelligence conference and the International Joint Conference on Artificial Intelligence. “If I just published a book with that name AGI, it possibly would have just come and gone,” says Goertzel. “But the conference was circling through every year, with more and more students coming.” Next is Legg, who took the term with him to DeepMind. “I think they were the first mainstream corporate entity to talk about AGI,” says Goertzel. “It wasn’t the main thing they were harping on, but Shane and Demis would talk about it now and then. That was certainly a source of legitimation.”
When I first talked to Legg about AGI five years ago, he said: “Talking about AGI in the early 2000s put you on the lunatic fringe … Even when we started DeepMind in 2010, we got an astonishing amount of eye-rolling at conferences.” But by 2020 the wind had changed. “Some people are uncomfortable with it, but it’s coming in from the cold,” he told me. The third thing Goertzel points to is the overlap between early AGI evangelists and Big Tech power brokers. In the years between shutting down Webmind and publishing that AGI book, Goertzel did some work with Peter Thiel at Thiel’s hedge fund Clarium Capital. “We talked a bunch,” says Goertzel. He recalls spending a day with Thiel at the Four Seasons in San Francisco. “I was trying to drum AGI into his head,” says Goertzel. “But then he was also hearing from Eliezer how AGI is going to kill everybody.” Enter the doomers That’s Eliezer Yudkowsky, another influential figure who has done at least as much as Goertzel, if not more, to push the idea of AGI. But unlike Goertzel, Yudkowsky thinks there’s a very high chance—99.5% is one number he throws out—that the development of AGI will be a catastrophe.   In 2000, Yudkowsky cofounded a nonprofit research outfit called the Singularity Institute for Artificial Intelligence (later renamed the Machine Intelligence Research Institute), which pretty quickly dedicated itself to preventing doomer scenarios. Thiel was an early benefactor.  At first, Yudkowsky’s ideas didn’t get much pickup. Recall that back then the idea of an all-powerful AI—let alone a dangerous one—was pure sci-fi. But in 2014, Nick Bostrom, a philosopher at the University of Oxford, published a book called Superintelligence. “It put the AGI thing out there,” says Goertzel. “I mean, Bill Gates, Elon Musk—lots of tech-industry AI people—read that book, and whether or not they agreed with his doomer perspective, Nick took Eliezer’s concepts and wrapped them up in a very acceptable way.”  
“All of these things gave AGI a stamp of acceptability,” Goertzel adds. “Rather than it being pure crackpot stuff from mavericks howling out in the wilderness.” STEPHANIE ARNETT/MIT TECHNOLOGY REVIEW | PUBLIC DOMAIN Yudkowsky has been banging the same drum for 25 years; many engineers at today’s top AI companies grew up reading and discussing his views online, especially on LessWrong, a popular hub for the tech industry’s fervent community of rationalists and effective altruists. Today, those views are more popular than ever, capturing the imagination of a younger generation of doomers like David Krueger, a researcher at the University of Montreal who previously served as research director at the UK’s AI Security Institute. “I think we are definitely on track to build superhuman AI systems that will kill everybody,” Krueger tells me. “And I think that’s horrible and we should stop immediately.” Yudkowsky gets profiled by the likes of the New York Times, which bills him as “Silicon Valley’s version of a doomsday preacher.” His new book, If Anyone Builds It, Everyone Dies, written with Nate Soares, president of the Machine Intelligence Research Institute, lays out wild claims, with little evidence, that unless we pull the plug on development, near-future AGI will lead to global Armageddon. The pair’s position is extreme: They argue that an international ban should be enforced at all costs, up to and including the point of nuclear retaliation. After all, “datacenters can kill more people than nuclear weapons,” Yudkowsky and Soares write.
This stuff is no longer niche. The book is an NYT bestseller and comes with endorsements from national security experts such as Suzanne Spaulding, a former US Department of Homeland Security official, and Fiona Hill, former senior director of the White House National Security Council, who now advises the UK government; celebrity scientists such as Max Tegmark and George Church; and other household names, including Stephen Fry, Mark Ruffalo, and Grimes. Yudkowsky now has a megaphone.  Still, it is those early quiet words in certain ears that may prove most consequential. Yudkowsky is credited with introducing Thiel to DeepMind’s founders, after which Thiel became one of the first big investors in the company. Having merged with Google, it is now the in-house AI lab for the tech colossus Alphabet.  Alongside Musk, Thiel was also instrumental in setting up OpenAI in 2015, sinking millions into a startup founded on the singular ambition to build AGI—and make it safe. In 2023, OpenAI CEO Sam Altman posted on X: “eliezer has IMO done more to accelerate AGI than anyone else. certainly he got many of us interested in AGI.” Yudkowsky might one day deserve the Nobel Peace Prize for that, Altman added. But by this point, Thiel had apparently grown wary of the “AI safety people” and the power they were gaining. “You don’t understand how Eliezer has programmed half the people in your company to believe in that stuff,” he is reported to have told Altman at a dinner party in late 2023. “You need to take this more seriously.” Altman “tried not to roll his eyes,” according to Wall Street Journal reporter Keach Hagey. OpenAI is now the most valuable private company in the world, worth half a trillion dollars.  And the transformation is complete: Like all the most powerful conspiracies, AGI has slipped into the mainstream and taken hold.     The great AGI conspiracy  The term “AGI” may have been popularized less than 20 years ago, but the mythmaking behind it has been there since the start of the computer age—a cosmic microwave background of chutzpah and marketing.  Alan Turing asked if machines could think only five years after the first electronic computer, ENIAC, was built in 1945. And here’s Turing a little later, in a 1951 radio broadcast: “It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits. At some stage therefore we should have to expect the machines to take control.” Then, in 1955, the computer scientist John McCarthy and his colleagues applied for US government funding to create what they fatefully chose to call “artificial intelligence”—a canny spin, given that computers at the time were the size of a room and as dumb as a thermostat. Even so, as McCarthy wrote in that funding application: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” It’s this myth that’s the root of the AGI conspiracy. A smarter-than-human machine that can do it all is not a technology. It’s a dream, unmoored from reality. Once you see that, other parallels with conspiracy thinking start to leap out. It’s impossible to debunk a shape-shifting idea like AGI.  Talking about AGI can sometimes feel like arguing with an enthusiastic Redditor about what drugs (or particles in the sky) are controlling your mind. Each point has a counterpoint that tries to chip away at your own sense of what’s true. Ultimately, it’s a clash of worldviews, not an exchange of evidence-based reason. AGI is like that, too—it’s slippery.  Part of the issue is that despite all the money, all the talk, nobody knows how to build it. More than that: Most people don’t even agree on what AGI really is—which helps explain how people can get away with telling us it can both save the world and end it. At the core of most definitions you’ll find the idea of a machine that can match humans on a wide range of cognitive tasks. (And remember, superintelligence is AGI’s shiny new upgrade: a machine that can outmatch us.) But even that’s easy to pull apart: What humans are we talking about? What kind of cognitive task? And how wide a range? “There’s no real definition of it,” says Christopher Symons, chief artificial intelligence scientist at the AI health-care startup Lirio and former head of the computer science and math division at Oak Ridge National Laboratory. “If you say ‘human-level intelligence,’ that could be an infinite number of things—everybody’s level of intelligence is slightly different.”  And so, says Symons, we’re in this weird race to build … what, exactly? “What are you trying to get it to do?” In 2023, a team of researchers at Google DeepMind, including Legg, had a go at categorizing various definitions that people had proposed for AGI. Some said that a machine had to be able to learn; some said that it had to be able to make money; some said that it had to have a body and move about in the world (and maybe make coffee).   Legg told me that when he’d suggested the term to Goertzel for the title of his book, the hand-waviness had been kind of the point. “I didn’t have an especially clear definition. I didn’t really feel it was necessary,” he said at the time. “I was actually thinking of it more as a field of study, rather than an artifact.” So, I guess we’ll know it when we see it? The problem is that some people think they’ve seen it already. In 2023, a team of Microsoft researchers put out a paper in which they described their experiences playing around with a prerelease version of OpenAI’s large language model GPT-4. They called it “Sparks of Artificial General Intelligence”—and it polarized the industry.  It was a moment when a lot of researchers were blown away and trying to come to terms with what they were seeing. “Shit was working better than they had expected it to,” says Goertzel. “The concept of AGI genuinely started to seem more plausible.” And yet for all of LLMs’ remarkable wordplay, Goertzel doesn’t think that they do in fact contain sparks of AGI. “It’s a little surprising to me that some people with a deep technical understanding of how these tools work under the hood still think that they could become human-level AGI,” he says. “On the other hand, you can’t prove it’s not true.” And there it is: You can’t prove it’s not true. “The idea that AGI is coming and that it’s right around the corner and that it’s inevitable has licensed a great many departures from reality,” says the University of Edinburgh’s Vallor. “But we really don’t have any evidence for it.” Conspiracy thinking looms again. Predictions about when AGI will arrive are made with the precision of numerologists counting down to the end of days. With no real stakes in the game, deadlines come and go with a shrug. Excuses are made and timelines are adjusted yet again. We saw this when OpenAI released the much-hyped GPT-5 this summer. AI stans were disappointed that the new version of the company’s flagship technology wasn’t the step change they expected. But instead of seeing that as evidence that AGI wasn’t attainable—or attainable with an LLM, at least—believers pushed out their predictions for how soon AGI would come. It was coming—just, you know, next time. Maybe they’re right. Or maybe people will pick whatever evidence they can to defend an idea and overlook evidence that counts against it. Jeremy Cohen, who studies conspiracy thinking in technology circles at McMaster University in Canada, calls this imperfect evidence gathering—a hallmark of conspiracy thinking. Cohen started his research career in the Arizona desert, studying a community called People Unlimited that believed its members were immortal. The conviction was impervious to contrary evidence. When its members died of natural causes (including two of its founders), the thinking was that they must have deserved it. “The general consensus was that every death was a suicide,” says Cohen. “If you are immortal and you get cancer and you die—well, you must have done something wrong.” Cohen has since been focused on transhumanism (the idea that technology can help humans push past their natural limitations) and AGI. “I am seeing a lot of parallels. There are forms of magical thinking that I think is a part of the popular imagination around AGI,” he says. “It connects really well to the kinds of religious imaginaries that you see in conspiracy thinking today.” The believers are in on the AGI secret.   Maybe some of you think I’m an idiot: You don’t get it at all lol. But that’s kind of my point. There are insiders and outsiders. When I talk to researchers or engineers who are happy to drop AGI into the conversation as a given, it’s like they know something I don’t. But nobody’s ever been able to tell me what that something is.  The truth is out there, if you know where to look. Conspiracy theories are primarily concerned about revealing a hidden truth, Cohen tells me: “It’s a really fundamental part of conspiracy thinking, and that’s absolutely something that you see in the way people talk about AGI,” he says.  Last year, a 23-year-old former OpenAI staffer turned investor, Leopold Aschenbrenner, published a much-dissected 165-page manifesto titled “Situational Awareness.” You don’t need to read it to get the idea: You either see the truth of what’s coming or you don’t. And you don’t need cold, hard facts, either—it’s enough to feel it. Those who don’t just haven’t seen the light.   This idea stalked the periphery of my conversation with Goertzel, too. When I pushed him on why people are skeptical of AGI, for instance, he said: “Before every major technical achievement, from human flight to electrical power, loads of wise pundits would tell you why it was never going to happen. The fact is, most people only believe what they see in front of their faces.”  That makes AGI sound like an article of faith. I put that to Krueger, who believes AGI’s arrival is maybe five years out. He scoffed: “I think that’s completely backwards.” For him, the article of faith is the idea that it won’t happen—it’s the skeptics who continue to deny the obvious. (Even so, he hedges: No one knows for sure, he says, but there’s no obvious reason that AGI won’t come.)  Hidden truths bring truth seekers, bent on revealing what they’ve been able to see all along. With AGI, though, it’s not enough to uncover something hidden. Here, revelation requires an unprecedented act of creation. If you believe AGI is achievable, then you believe that those making it are midwives to machines that will match or surpass human intelligence. “The idea of giving birth to machine gods is obviously very flattering to the ego,” says Vallor. “It’s an incredibly seductive thing to think that you yourself are laying the early foundations for that transcendence.”  It’s yet another overlap with conspiracy thinking. Part of the draw is the desire for a sense of purpose in an otherwise messy world that can feel meaningless—the longing to be a person of consequence.  Krueger, who is based in Berkeley, says he knows people working on AI who see the technology as our natural successor. “They view it as akin to having children or something,” he says. “Side note: they usually don’t have children.” AGI will be our one true savior (or it’ll bring the apocalypse).  Cohen sees parallels between many modern conspiracy theories and the New Age movement, which reached its peak of influence in the 1970s and ’80s. Adherents believed humanity was on the cusp of unlocking an era of spiritual well-being and expanded consciousness that would usher in a more peaceful and prosperous world. In a nutshell, the idea was that by engaging in a set of pseudo-religious practices, including astrology and the careful curation of crystals, humans would transcend their limitations and enter a kind of hippie utopia. Today’s tech industry is built on compute, not crystals, but its sense of what’s at stake is no less transcendent: “You know, this idea that there is going to be this fundamental shift, there’s going to be this millenarian turn where we end up in a techno-utopian future,” says Cohen. “And the idea that AGI is going to ultimately allow humanity to overcome the problems that face us.” In many people’s telling, AGI will arrive all at once. Incremental advances in AI will stack up until, one day, AI will be good enough to start making better AI by itself. At which point—FOOM—it will advance so rapidly that AGI will arrive in what’s often called an intelligence explosion, leading to a point of no return known as the Singularity, a goofy term that’s been popular in AGI circles for years. Co-opting a concept from physics, the science fiction author Vernor Vinge first introduced the idea of a technological singularity in the 1980s. Vinge imagined an event horizon on the path of technological progress beyond which humans would be fast outstripped by the exponential self-improvement of the machines they had created.  Call it the AI Big Bang—which, again, gives us a before and an after, a transcendent moment when humanity as we know it changes forever (for good or bad). “People imagine it as an event,” says Grace from AI Impacts. For Vallor, this belief system is notable for the way that a faith in technology has replaced a faith in humans. Despite the woo-woo, New Age thinking was at least motivated by the idea that people had what it took to change the world by themselves, if they could only tap into it. With the pursuit of AGI, we’ve left that self-belief behind and bought into the idea that only technology can save us, she says.   That’s a compelling—even comforting—thought for many people. “We’re in an era where other paths to material improvement of human lives and our societies seem to have been exhausted,” Vallor says.  Technology once promised a route to a better future: Progress was a ladder that we would climb toward human and social flourishing. “We’ve passed the peak of that,” says Vallor. “I think the one thing that gives many people hope and a return to that kind of optimism about the future is AGI.” Push this idea to its conclusion and, again, AGI becomes a kind of god—one that can offer relief from earthly suffering, says Vallor. Kelly Joyce, a sociologist at the University of North Carolina who studies how cultural, political, and economic beliefs shape the way we think about and use technology, sees all these wild predictions about AGI as something more banal: part of a long-term pattern of overpromising from the tech industry. “What’s interesting to me is that we get sucked in every time,” she says. “There is a deep belief that technology is better than human beings.” Joyce thinks that’s why, when the hype kicks in, people are predisposed to believe it. “It’s a religion,” she says. “We believe in technology. Technology is God. It’s really hard to push back against it. People don’t want to hear it.” How AGI hijacked an industry The fantasy of computers that can do almost anything a person can is seductive. But like many pervasive conspiracy theories, it has very real consequences. It has distorted the way we think about the stakes behind the current technology boom (and potential bust). It may have even derailed the industry, sucking resources away from more immediate, more practical application of the technology. More than anything else, it gives us a free pass to be lazy. It fools us into thinking we might be able to avoid the actual hard work needed to solve intractable, world-spanning problems—problems that will require international cooperation and compromise and expensive aid. Why bother with that when we’ll soon have machines to figure it all out for us? Consider the resources being sunk into this grand project. Just last month, OpenAI and Nvidia announced an up-to-$100 billion partnership that would see the chip giant supply at least 10 gigawatts of ChatGPT’s insatiable demand. That’s higher than nuclear power plant numbers. A bolt of lightning might release that much energy. The flux capacitor inside Dr. Emmett Brown’s DeLorean time machine only required 1.2 gigawatts to send Marty back to the future. And then, only two weeks later, OpenAI announced a second partnership with chipmaker AMD for another six gigawatts of power. Promoting the Nvidia deal on CNBC, Altman, straight-faced, claimed that without this kind of data center buildout, people would have to choose between a cure for cancer and free education. “No one wants to make that choice,” he said. (Just a few weeks later, he announced that erotic chats would be coming to ChatGPT.) Add to those costs the loss of investment in more immediate technology that could change lives today and tomorrow and the next day. “To me it’s a huge missed opportunity,” says Lirio’s Symons, “to put all these resources into solving something nebulous when we already know there’s real problems that we could solve.”  But that’s not how the likes of OpenAI needs to operate. “With people throwing so much money at these companies, they don’t have to do that,” Symons says. “If you’ve got hundreds of billions of dollars, you don’t have to focus on a practical, solvable project.” Despite his steadfast belief that AGI is coming, Krueger also thinks the industry’s single-minded pursuit of it means that potential solutions to real problems, such as better health care, are being ignored. “This AGI stuff—it’s nonsense, it’s a distraction, it’s hype,” he tells me.  And there are consequences for the way governments support and regulate technology (or don’t). Tina Law, who studies technology policy at the University of California–Davis, worries that policymakers are getting lobbied about the ways AI will one day kill us all, instead of addressing real concerns about the ways AI could impact people’s lives in immediate and material ways today. Inequality has been sidetracked by existential risk. “Hype is a lucrative strategy for tech firms,” says Law. A big part of that hype is the idea that what’s happening is inevitable: If we don’t build it, someone else will. “When something is framed as inevitable,” Law says, “people doubt not only whether they should resist but also whether they have the capacity to do so.” Everyone gets locked in.  The AGI distortion field isn’t limited to tech policy, says Milton Mueller at the Georgia Institute of Technology, who works on technology policy and regulation. The race to AGI gets compared to the race to the atomic bomb, he says. “So whoever gets it first is going to have ultimate power over everybody else. That’s a crazy and dangerous idea that really will distort our approach to foreign policy.”  There’s a business incentive for companies (and governments) to push the myth of AGI, says Mueller, because they can then claim that they will be the first to get there. But because they’re running a race in which nobody has agreed on the finish line, the myth can be spun as long as it’s useful. Or as long as investors are willing to buy into it.  It’s not hard to see how this plays out. It’s not utopia or hell—it’s OpenAI and its peers making a whole lot more money. The great AGI conspiracy, concluded  And maybe that brings us back to the whole conspiracy thing—and a late-game twist in this tale. So far we’ve ignored one popular feature of conspiracy thinking: that there’s a group of powerful figures pulling the levers behind the scenes and that, by seeking the truth, believers can expose this elite cabal.  Sure, the people feeling the AGI aren’t publicly accusing any Illuminati or WEF-like force of preventing the AGI future or withholding its secrets.  But what if there are, in fact, shadowy puppet masters here—and they’re the very people who have pushed the AGI conspiracy hardest all along? The kings of Silicon Valley are throwing everything they can get at building AGI for profit. The myth of AGI serves their interests more than anybody else’s.  As one senior executive at an AI company said to us recently, AGI always needs to be six months to a year away, because if it’s any further than that, you won’t be able to recruit people from Jane Street, and if it’s closer to already here, then what’s the point?  As Vallor puts it: “If OpenAI says they’re building a machine that’s going to make corporations even more powerful than they are today, that isn’t going to get the kind of public buy-in that they need.”  Remember: You create a god and you become like one yourself. Krueger says there’s a line of thinking running through Silicon Valley in which building AI is a way to seize huge amounts of power. (It’s one of the premises of Aschenbrenner’s “Situational Awareness,” for example.) “You know, we’re going to have this godlike power and we’re going to have to figure out what to do with it,” says Krueger. “A lot of people think if they get there first, they can basically take over the world.” “They’re putting so much effort into selling their vision of a future with AGI in it, and they’re having a pretty good amount of success because they have so much power,” he adds. Goertzel, for one, is almost lamenting how successful the maybe-cabal has been. He’s actually starting to miss life on the fringes. “In my generation, you had to have a lot of vision to want to work on AGI, and you had to be very stubborn,” he says. “Now it’s almost, like, what your grandma tells you to do to get a job instead of being a business major.” “It’s disorienting that this stuff is so broadly accepted,” he says. “It almost gives me the desire to go work on something else that not so many people are doing.” He’s half joking (I think): “Obviously, putting the finishing touches to AGI is more important than gratifying my preference to be out on the frontier.” But I’m no clearer on what exactly they’re putting the finishing touches on. What does it mean for technology in general if we fall so hard for the fairy tales? In a lot of ways, I think the whole idea of AGI is built on a warped view of what we should expect technology to do, and even what intelligence is in the first place. Stripped back to its essentials, the argument for AGI rests on the premise that one technology, AI, has gotten very good, very fast, and will continue to get better. But set aside the technical objections—what if it doesn’t continue to get better?—and you’re left with the claim that intelligence is a commodity you can get more of if you have the right data or compute or neural network. And it’s not.  Intelligence doesn’t come as a quantity you can just ratchet up and up. Smart people may be brilliant in one area and not in others. Some Nobel Prize winners are really bad at playing the piano or caring for their kids. Some very smart people insist that AGI is coming next year.  It’s hard not to wonder what will get its hooks into us next.  Before we ended our call, Goertzel told me about an event he’d just been to in San Francisco on AI consciousness and parapsychology: “ESP, precognition, and whatnot.” “That’s where AGI was 20 years ago,” he said. “Everyone thinks it’s batshit crazy.”

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In this exclusive subscirber-only ebook you’ll learn how the emissions from individual AI text, image, and video queries seem small—until you add up what the industry isn’t tracking and consider where it’s heading next.by James O’Donnell and Casey Crownhart May 20, 2025 Table of contents Part One: Making the model Part Two: A Query Part Three: Fuel and emissions Part Four: The future ahead Related stories:

In partnership withDatabricks Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI’s capabilities have been advancing at speed, and the advances have not slowed since generative AI’s breakthrough. For example, multimodality— the ability to process information not only as text but also as audio, video, and other unstructured formats—is becoming a common feature of AI models. AI’s capacity to reason and act autonomously has also grown, and organizations are now starting to work with AI agents that can do just that. Amid all the change, there remains a constant: the quality of an AI model’s outputs is only ever as good as the datathat feeds it. Data management technologies and practices have also been advancing, but the second edition of this study suggests that most organizations are not leveraging those fast enough to keep up with AI’s development. As a result of that and other hindrances, relatively few organizations are delivering the desired business results from their AI strategy. No more than 2% of senior executives we surveyed rate their organizations highly in terms of delivering results from AI. To determine the extent to which organizational data performance has improved as generative AI and other AI advances have taken hold, MIT Technology Review Insights surveyed 800 senior data and technology executives. We also conducted in-depth interviews with 15 technology and business leaders. Key findings from the report include the following:
• Few data teams are keeping pace with AI. Organizations are doing no better today at delivering on data strategy than in pre-generative AI days. Among those surveyed in 2025, 12% are self-assessed data “high achievers” compared with 13% in 2021. Shortages of skilled talent remain a constraint, but teams also struggle with accessing fresh data, tracing lineage, and dealing with security complexity—important requirements for AI success. • Partly as a result, AI is not fully firing yet. There are even fewer “high achievers” when it comes to AI. Just 2% of respondents rate their organizations’ AI performance highly today in terms of delivering measurable business results. In fact, most are still struggling to scale generative AI. While two thirds have deployed it, only 7% have done so widely. Download the report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Mathematics is the foundational language of the universe, providing the tools to describe everything from the laws of physics to the intricacies of biology and the logic of computer science. For centuries, its frontiers have been expanded by human ingenuity alone. At Google DeepMind, we believe AI can serve as a powerful tool to collaborate with mathematicians, augmenting creativity and accelerating discovery.Today, we’re introducing the AI for Math Initiative, supported by Google DeepMind and Google.org. It brings together five of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research.The inaugural partner institutions are:Imperial College LondonInstitute for Advanced StudyInstitut des Hautes Études Scientifiques (IHES)Simons Institute for the Theory of Computing (UC Berkeley)Tata Institute of Fundamental Research (TIFR)The initiative’s partners will work towards the shared goals of identifying the next generation of mathematical problems ripe for AI-driven insights, building the infrastructure and tools to power these advances and, ultimately, accelerating the pace of discovery.Google’s support includes funding from Google.org and access to Google DeepMind’s state-of-the-art technologies, such as an enhanced reasoning mode called Gemini Deep Think, our agent for algorithm discovery, AlphaEvolve, and our formal proof completion system, AlphaProof. The initiative will create a powerful feedback loop between fundamental research and applied AI, opening the door to deeper partnerships.A pivotal moment for AI and mathematicsThe AI for Math Initiative comes at a time of remarkable progress in AI’s reasoning capabilities; our own work has seen rapid advancement in recent months.In 2024, our AlphaGeometry and AlphaProof systems achieved a silver-medal standard at the International Mathematical Olympiad (IMO). More recently, our latest Gemini model, equipped with Deep Think, achieved a gold-medal level performance at this year’s IMO, perfectly solving five of the six problems and scoring 35 points.And we’ve seen further progress with another of our methods, AlphaEvolve, which was applied to over 50 open problems in mathematical analysis, geometry, combinatorics and number theory and improved the previously best known solutions in 20% of them. In mathematics and algorithm discovery, it has invented a new, more efficient method for matrix multiplication — a core calculation in computing. For the specific problem of multiplying 4×4 matrices, AlphaEvolve discovered an algorithm using just 48 scalar multiplications, breaking the 50-year-old record set by Strassen’s algorithm in 1969. In computer science, it helped researchers discover new mathematical structures that show certain complex problems are even harder for computers to solve than we previously knew. This gives us a clearer and more precise understanding of computational limits, which will help guide future research.This rapid progress is a testament to the fast-evolving capabilities of AI models. We hope this new initiative can explore how AI can accelerate discovery in mathematical research, and tackle harder problems.We are only at the beginning of understanding everything AI can do, and how it can help us think about the deepest questions in science. By combining the profound intuition of world-leading mathematicians with the novel capabilities of AI, we believe new pathways of research can be opened, advancing human knowledge and moving toward new breakthroughs across the scientific disciplines.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. DeepSeek may have found a new way to improve AI’s ability to remember The news: An AI model released by Chinese AI company DeepSeek uses new techniques that could significantly improve AI’s ability to “remember.”How it works: The optical character recognition model works by extracting text from an image and turning it into machine-readable words. This is the same technology that powers scanner apps, translation of text in photos, and many accessibility tools.Why it matters: Researchers say the model’s main innovation lies in how it processes information—specifically, how it stores and retrieves data. Improving how AI models “remember” could reduce how much computing power they need to run, thus mitigating AI’s large (and growing) carbon footprint. Read the full story. —Caiwei Chen
The AI Hype Index: Data centers’ neighbors are pivoting to power blackouts
Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition of the index here. Roundtables: seeking climate solutions in turbulent times Yesterday we held a subscriber-only conversation exploring how companies are pursuing climate solutions amid political shifts in the US.Our climate reporters James Temple and Casey Crownhart sat down with our science editor Mary Beth Griggs to dig into the most promising climate technologies right now. Watch the session back here! MIT Technology Review Narrated: Supershoes are reshaping distance running “Supershoes” —which combine a lightweight, energy-­returning foam with a carbon-fiber plate for stiffness—have been behind every broken world record in distances from 5,000 meters to the marathon since 2020. To some, this is a sign of progress—for both the field as a whole and for athletes’ bodies. Still, some argue that they’ve changed the sport too quickly.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Hurricane Melissa may be the Atlantic Ocean’s strongest on recordThere’s little doubt in scientists’ minds that human-caused climate change is to blame. (New Scientist $)+ While Jamaica is largely without power, no deaths have been confirmed. (BBC)+ The hurricane is currently sweeping across Cuba. (NYT $)+ Here’s what we know about hurricanes and climate change. (MIT Technology Review) 2 Texas is suing Tylenol over the Trump administration’s autism claimsEven though the scientific evidence is unfounded. (NY Mag $)+ The lawsuit claims the firm violated Texas law by claiming the drug was safe. (WP $) 3 Two US Senators want to ban AI companions for minorsThey want AI companies to implement age-verification processes, too. (NBC News)+ The looming crackdown on AI companionship. (MIT Technology Review) 3 Trump’s “golden dome” plan is seriously flawed It’s unlikely to offer anything like the protection he claims it will. (WP $)+ Why Trump’s “golden dome” missile defense idea is another ripped straight from the movies. (MIT Technology Review)
4 The Trump administration is backing new nuclear plantsTo—surprise surprise—power the AI boom. (NYT $)+ The grid is straining to support the excessive demands for power. (Reuters)+ Can nuclear power really fuel the rise of AI? (MIT Technology Review) 5 Uber’s next fleet of autonomous cars will contain Nvidia’s new chips Which could eventually make it cheaper to hail a robotaxi. (Bloomberg $)+ Nvidia is also working with a company called Lucid to bring autonomous cars to consumers. (Ars Technica)
6 Weight loss drugs are becoming more commonplace across the worldSemaglutide patents are due to expire in Brazil, China and India next year. (Economist $)+ We’re learning more about what weight-loss drugs do to the body. (MIT Technology Review) 7 More billionaires hail from America than any other nationThe majority of them have made their fortunes working in technology. (WSJ $)+ China is closing in on America’s global science lead. (Bloomberg $) 8 Australian police are developing an AI tool to decode Gen Z slangIt’s in a bid to combat the rising networks of young men targeting vulnerable girls online. (The Guardian) 9 This robot housekeeper is controlled remotely by a human 🤖Nothing weird about that at all… (WSJ $)+ The humans behind the robots. (MIT Technology Review) 10 Cameo is suing OpenAIIt’s unhappy about Sora’s new Cameo feature. (Reuters)
Quote of the day “I don’t believe we’re in an AI bubble.” —Jensen Haung, Nvidia’s CEO, conveniently dismisses the growing concerns around the AI hype train, Bloomberg reports.
One more thing How to befriend a crowCrows have become minor TikTok celebrities thanks to CrowTok, a small but extremely active niche on the social video app that has exploded in popularity over the past two years. CrowTok isn’t just about birds, though. It also often explores the relationships that corvids—a family of birds including crows, magpies, and ravens—develop with human beings. They’re not the only intelligent birds around, but in general, corvids are smart in a way that resonates deeply with humans. But how easy is it to befriend them? And what can it teach us about attention, and patience, in a world that often seems to have little of either? Read the full story. —Abby Ohlheiser We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Congratulations to Flava Flav, who’s been appointed Team USA’s official hype man for the 2026 Winter Olympics!+ Why are Spirographs so hypnotic? Answers on a postcard.+ I love this story—and beautiful photos—celebrating 50 years of the World Gay Rodeo.+ Axolotls really are remarkable little creatures.