THE ATLANTIC – Technology | Internet & Technology

Late last month, a large crowd gathered in downtown San Francisco to demand that the AI industry stop developing more powerful bots. Holding signs and banners reading Stop the AI Race and Don’t Build Skynet, the protesters marched through the city and gave speeches outside the offices of Anthropic, OpenAI, and xAI. The crowd demanded that these companies halt efforts to create superintelligent machines—and, in particular, AI models that can develop future AI models. Such a technology, attendees said, could extinguish all human life.At AI protests and happy hours, inside start-ups and major companies, the tech world is in a frenzy over the same thing: Computers that make themselves smarter. Over the past year, the top AI companies have taken to loudly bragging about internal efforts to automate their own research. OpenAI recently released a new model it described as “instrumental in creating itself.” Within the next six months, the company aims to debut what it has described as an “intern-level AI research assistant.” Meanwhile, Anthropic says that as much as 90 percent of the company’s code is already written by Claude.“We are starting to see AI progress feed back on itself,” Nick Bostrom, an influential Swedish philosopher who studies AI risk, told us. Within Silicon Valley, many insiders believe that we are teetering on the precipice of a world in which AI can rapidly improve its own capabilities. Instead of waiting for months between new machine-learning breakthroughs, we might wait weeks. Imagine AI advancing faster and faster.The idea of self-improving bots is nothing new. When the statistician I. J. Good first introduced the concept of recursive self-improvement in the 1960s, he wrote that machines capable of training their own, even more capable successors would be “the last invention” society ever needed to make. But just a few years ago, any notion of actually making such AI models was on the back burner. When ChatGPT couldn’t reliably add and subtract, let alone search the web, the notion that AI programs would soon be able to do world-class machine-learning research seemed laughable. Even as tech companies made claims about the imminent arrival of “artificial general intelligence,” the capabilities needed for a bot to accelerate or even direct AI research seemed to exceed those of AGI.[Read: Do you feel the AGI yet?]Now, as AI models have become significantly better at coding, Silicon Valley has become hooked on the idea of self-improving machines.… [TheTopNews] Read More.

“Come get ready with me for the day,” a young blond woman says over footage of herself making her bed, arranging her pillows, and weighing her clothing choices. The video is just like any other lifestyle content that influencers post to Instagram and TikTok—right up until she whips out her phone and scrolls through the Kalshi app. “I use it to check the weather to help me pick out an outfit for the day,” she says, modeling a black spandex romper for the camera. “Go ahead and check out the app link below.”Recently, my Instagram feed has been haunted by women explaining how much they enjoy betting on elections, the pop-music charts, and Dancing With the Stars. They are advertising prediction markets such as Kalshi and Polymarket, which let users wager on virtually anything. “The boys can do their parlays and use words I’ve never heard of. But the girls can use their pop culture and educated guesses to make decisions and trade on Kalshi,” a woman says in a TikTok on one of the company’s accounts. Her caption assures me: “Kalshi is for the girls!!!!”So far though, it is not. Prediction markets have a dude problem. Though these sites offer all sorts of wagers—where will Taylor Swift get married? Who will win Survivor?—they have largely become yet another place for men to bet on football and March Madness. In the past six months, 88 percent of trades on Kalshi have been about sports, according to the investment firm Paradigm. The second-largest category, at about 6 percent, is crypto (which is arguably even more bro-ey).[Read: You’ve never seen Super Bowl betting like this before]In an apparent attempt to bridge the gap, both Polymarket and Kalshi are running social-media campaigns that parrot the language of female empowerment and girlish memes. “Girl math says if I make $10 predicting real-life stuff, that coffee was technically free,” a girl in thick-framed glasses says in an ad that Kalshi ran on Facebook and Instagram. “If I’m already scrolling news or pop culture anyway, might as well turn my hot takes into some free iced coffees.” She adds, “It’s kind of addicting, but in a fun way.” (The video has since been removed for not having a necessary ad disclosure.) Some posts, like this one, are advertisements from the companies themselves; some are paid influencer partnerships; and some are either undisclosed partnerships or made by… [TheTopNews] Read More.

Recently, a Costco in Florida instituted a new store policy. An employee told me that he was asked to open up every desktop computer displayed in the electronics section and remove the memory chips. Otherwise, the RAM harvesters would get them. Elsewhere, criminal groups are misdirecting trucks carrying RAM in order to loot them. All of this is happening because of a generational shortage of a part used in practically every electronic gadget on Earth.RAM is your device’s short-term memory—storing the information it needs to handle any active tasks. (RAM stands for “random-access memory.”) To put this in intimately familiar terms, it is what your computer runs out of when you have too many browser tabs open. And right now, the price of RAM is skyrocketing. From September to February, the price of a single 64GB stick of RAM went from roughly $250 to more than $1,000.Gamers who build their own juiced computers were among the first to notice that something was off. Starting in the fall, it became so difficult for them to acquire memory sticks that they have given a name to this crisis: RAMageddon. Now it’s quickly becoming everyone’s problem. In December, Dell jacked the prices of some of its computers by hundreds of dollars because of what its COO has referred to as “this memory crisis, shortage, whatever you want to call it.” Earlier this month, for the same reason, Lenovo raised prices on some of its products, including the popular ThinkPad.This seems to be only the beginning. Matteo Rinaldi, the head of a global semiconductor-research institute run by Northeastern University, told me he recently asked a colleague what new laptop he should buy. “He told me right away, ‘Well, you know, it almost doesn’t matter which one,’” Rinaldi said. “‘Just decide you want to buy now, because prices are going up.’”RAM is suddenly so expensive because memory is powering the AI boom. Data centers require huge amounts to run the models that underlie AI tools such as ChatGPT and Claude—especially as they become capable of handling more complicated tasks. This year, a group of tech giants—Amazon, Alphabet, Meta, Microsoft, and Oracle—is set to collectively spend half a trillion dollars on the AI build-out. Roughly a third of that money is being spent on memory alone, according to Dylan Patel, the founder of SemiAnalysis, a popular semiconductor-research firm.[Read: Welcome to a multidimensional economic disaster]The insatiable demand… [TheTopNews] Read More.

Thore Graepel may have been the first human to be vanquished by a superintelligence. In 2015, on his first day as a researcher at Google DeepMind, he was challenged to play against the earliest iteration of AlphaGo—a computer program developed by DeepMind that would prove so effective at the ancient-Chinese game of weiqi (or Go, as it is commonly known in the West) that it changed how humans play it, and then upended the field of AI itself.When Graepel faced it, AlphaGo was just a “baby” project, as he put it to me, and he was an accomplished amateur player. But it still took him down. Then, the following year, AlphaGo—now fully developed—plowed through a number of human champions, ultimately crushing Lee Sedol, widely considered the best player in the world, with a match score of 4–1. This month marked the tenth anniversary of that victory.For decades, developing a program that plays Go at an elite level was an infamous problem in computer science. Many considered it unsolvable—far harder than developing a similar program for chess, in which the supercomputer DeepBlue beat the world champion in 1997. In Go, two players take turns positioning stones on a 19-by-19 grid, and their movements are relatively unrestricted. In chess, which has a far smaller grid, a rook can move only horizontally and a bishop only diagonally, but Go pieces can be placed on any open space. The number of possible Go positions is so high that it cannot be easily expressed in words; it is higher than the number of atoms in the observable universe, and orders of magnitude higher than the number of possible chess games. Today, the technical frameworks and approaches that allowed an algorithm to excel at this board game have translated fairly directly into bots that can write advanced code, help tackle open problems in mathematics, and replicate scientific discoveries from scratch.Generative AI is living in AlphaGo’s shadow. Beyond the actual models, “conceptual things emerged from the whole AlphaGo experience which essentially entered the AI vocabulary,” Pushmeet Kohli, the vice president of science and strategic initiatives at Google DeepMind, told me. In many ways, Go and chess provide ideal templates for understanding how the AI boom has unfolded—and a guide for what it may yet wreak.DeepMind’s innovation was to essentially pair two algorithms: one AI model to propose moves and a second model to judge whether a move… [TheTopNews] Read More.

The global economy has become dependent on the AI industry. Trillions of dollars are being invested into the technology and the infrastructure it relies on; in the final months of 2025, functionally all economic growth in the United States came from AI investments. This would be risky even in ideal conditions. And we are very far from ideal conditions.Much of the AI supply chain—chips, data centers, combustion turbines, and so on—relies on key materials that are produced in or transported through just a few places on Earth, with little overlap. In particular, the industry is highly dependent on the Middle East, which has been destabilized by the war in Iran. A global energy shock seems all but certain to come soon—the kind where even the best-case scenario is a disaster. The war could grind the AI build-out to a halt. This would be devastating for the tech firms that have issued historic amounts of debt to race against their highly leveraged competitors, and it would be devastating for the private lenders and banks that have been buying up that debt in the hope of ever bigger returns.For the better part of the past year, Wall Street analysts and tech-industry observers have fretted publicly about an AI bubble. The fear is that too much money is coming in too fast and that generative-AI companies still have not offered anything close to a viable business model. If growth were to stall or the technology were to be seen as failing to deliver on its promises, the bubble might burst, triggering a chain reaction across the financial system. Everyone—big banks, private-equity firms, people who have no idea what’s mixed into their 401(k)—would be hit by the AI crash.Until recently, that kind of crash felt hypothetical; today, it feels plausible and, to some, almost inevitable. “What’s unusual about this, unlike commercial real estate during the global financial crisis,” Paul Kedrosky, an investor and financial consultant, told us, “is all of these interlocking points of fragility.”[Read: Here’s how the AI crash happens]Perhaps the clearest examples are advanced memory and training chips, which are among the most important—and are by far the most expensive—components of training any AI model. Currently, most of them are produced by two companies in South Korea and one in Taiwan. These countries, in turn, get a large majority of their crude oil and much of their liquefied natural gas—which help fuel… [TheTopNews] Read More.