On February 28, OpenAI announced it had reached a deal that will allow the US military to use its technologies in classified settings. CEO Sam Altman said the negotiations, which the company began pursuing only after the Pentagon’s public reprimand of Anthropic, were “definitely rushed.” In its announcements, OpenAI took great pains to say that it had not caved to allow the Pentagon to do whatever it wanted with its technology. The company published a blog post explaining that its agreement protected against use for autonomous weapons and mass domestic surveillance, and Altman said the company did not simply accept the same terms that Anthropic refused.  You could read this to say that OpenAI won both the contract and the moral high ground, but reading between the lines and the legalese makes something else clear: Anthropic pursued a moral approach that won it many supporters but failed, while OpenAI pursued a pragmatic and legal approach that is ultimately softer on the Pentagon.  It’s not yet clear if OpenAI can build in the safety precautions it promises as the military rushes out a politicized AI strategy during strikes on Iran, or if the deal will be seen as good enough by employees who wanted the company to take a harder line. Walking that tightrope will be tricky. (OpenAI did not immediately respond to requests for additional information about its agreement.)
But the devil is also in the details. The reason OpenAI was able to make a deal when Anthropic could not was less about boundaries, Altman said, but about approach. “Anthropic seemed more focused on specific prohibitions in the contract, rather than citing applicable laws, which we felt comfortable with,” he wrote.  OpenAI says one basis for its willingness to work with the Pentagon is simply an assumption that the government won’t break the law. The company, which has shared a limited excerpt of its contract, cites a number of laws and policies related to autonomous weapons and surveillance. They are as specific as a 2023 directive from the Pentagon on autonomous weapons (which does not prohibit them but issues guidelines for their design and testing) and as broad as the Fourth Amendment, which has supported protections for Americans against mass surveillance. 
However, the published excerpt “does not give OpenAI an Anthropic-style, free-standing right to prohibit otherwise-lawful government use,” wrote Jessica Tillipman, associate dean for government procurement law studies at George Washington University’s law school. It simply states that the Pentagon can’t use OpenAI’s tech to break any of those laws and policies as they’re stated today. The whole reason Anthropic earned so many supporters in its fight—including some of OpenAI’s own employees—is that they don’t believe these rules are good enough to prevent the creation of AI-enabled autonomous weapons or mass surveillance. And an assumption that federal agencies won’t break the law is little assurance to anyone who remembers that the surveillance practices exposed by Edward Snowden had been deemed legal by internal agencies and were ruled unlawful only after drawn-out battles (not to mention the many surveillance tactics allowed under current law that AI could expand). On this front, we’ve essentially ended up back where we started: allowing the Pentagon to use its AI for any lawful use.  OpenAI could say, as its head of national security partnerships wrote yesterday, that if you believe the government won’t follow the law, then you should also not be confident it would honor the red lines that Anthropic was proposing. But that’s not an argument against setting them. Imperfect enforcement doesn’t make constraints meaningless, and contract terms still shape behavior, oversight, and political consequences. OpenAI claims a second line of defense. The company says it maintains control over the safety rules governing its models and will not give the military a version of its AI stripped of those safety controls. “We can embed our red lines—no mass surveillance and no directing weapons systems without human involvement—directly into model behavior,” wrote Boaz Barak, an OpenAI employee Altman deputized to speak on the issue about X.  But the company doesn’t specify how its safety rules for the military differ from its rules for normal users. Enforcement is also never perfect, and it is especially unlikely to be when OpenAI is rolling out these protections in a classified setting for the first time and is expected to do so in just six months. There’s another question beneath all this: Should it be down to tech companies to prohibit things that are legal but that they find morally objectionable? The government certainly viewed Anthropic’s willingness to play this role as unacceptable. On Friday evening, eight hours before the US launched strikes in Tehran, Defense Secretary Pete Hegseth issued harsh remarks on X. “Anthropic delivered a master class in arrogance and betrayal,” he wrote, and echoed President Trump’s order for the government to cease working with the AI company after Anthropic sought to keep its model Claude from being used for autonomous weapons or mass domestic surveillance. “The Department of War must have full, unrestricted access to Anthropic’s models for every LAWFUL purpose,” Hegseth wrote. But unless OpenAI’s full contract will reveal more, it’s hard not to see the company as sitting on an ideological seesaw, promising that it does have leverage it will proudly use to do what it sees as the right thing while deferring to the law as the main backstop for what the Pentagon can do with its tech. There are three things to be watching here. One is whether this position will be good enough for OpenAI’s most critical employees. With AI companies spending so heavily on talent, it’s possible that some at OpenAI see in Altman’s justification an unforgivable compromise.

Second, there is the scorched-earth campaign that Hegseth has promised to wage against Anthropic. Going far beyond simply canceling the government’s contract with the company, he announced that it would be classified as a supply chain risk, and that “no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.” There is significant debate about whether this death blow is legally possible, and Anthropic has said it will sue if the threat is pursued. OpenAI has also come out against the move. Lastly, how will the Pentagon swap out Claude—the only AI model it actively uses in classified operations, including some in Venezuela—while it escalates strikes against Iran? Hegseth granted the agency six months to do so, during which the military will phase in OpenAI’s models as well as those from Elon Musk’s xAI. But Claude was reportedly used in the strikes on Iran hours after the ban was issued, suggesting that a phase-out will be anything but simple. Even if the months-long feud between Anthropic and the Pentagon is over (which I doubt it is), we are now seeing the Pentagon’s AI acceleration plan put pressure on companies to relinquish lines in the sand they had once drawn, with new tensions in the Middle East as the primary testing ground. If you have information to share about how this is unfolding, reach out to me via Signal (username: jamesodonnell.22).

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. I checked out one of the biggest anti-AI protests ever Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta and Google DeepMind, chanting slogans and waving signs. The march was organized by a coalition of two separate activist groups, Pause AI and Pull the Plug, who billed it as the largest protest of its kind yet.This is all familiar stuff. Researchers have been calling out the harms, both real and hypothetical, caused by generative AI— especially models such as OpenAI’s ChatGPT and Google DeepMind’s Gemini—for years. What’s changed is that those concerns are now being taken up by protest movements that can rally significant crowds of people to take to the streets and shout about it. Read the full story. —Will Douglas Heaven
We’re putting more stuff into space than ever. Here’s what’s up there.
Earth’s a medium-size rock with some water on top, enveloped by gases that keep everything that lives here alive. Just at the edge of that envelope begins a thin but dense layer of human-built, high-tech stuff. People started putting gear up there in 1957, and now it’s a real habit. Telescopes look up and out at the wild universe. Humans live in an orbiting metal bubble. In the last five years, the number of active satellites in space has increased from barely 3,000 to about 14,000—and climbing. And then there’s the garbage. Here’s a closer look at Earth’s ever-thickening shell of human-made matter—the anthroposphere. —Jonathan O’Callaghan This story is from the latest print issue of MIT Technology Review magazine. If you haven’t already, subscribe now to receive future issues once they land.  MIT Technology Review is a 2026 ASME finalist in reporting The American Society of Magazine Editors has named MIT Technology Review as a finalist for a 2026 National Magazine Award in the reporting category.  The shortlisted story—“We did the math on AI’s energy footprint. Here’s the story you haven’t heard”—is part of our Power Hungry package on AI’s energy burden. 

In a rigorous investigation, senior AI reporter James O’Donnell and senior climate reporter Casey Crownhart spent six months digging through hundreds of pages of reports, interviewing experts, and crunching the numbers. Read more about what they found out. What comes after the LLMs? The AI industry is organized around LLMs: tools, products, and business models. Yet many researchers believe the next breakthroughs may not look like language models at all. Join us for a LinkedIn Live discussion at 12.30pm ET on Tuesday March 3 to dive into the emerging directions that could define AI’s next era. Register here! The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The Pentagon wanted Anthropic to analyze bulk data collected from Americans It proved the sticking point in talks as OpenAI swooped in to ink a new deal. (The Atlantic $)+ Anthropic has vowed to legally challenge its “security risk” label. (FT $)+ Here’s a blow-by-blow look at how negotiations fell apart. (NYT $)+ Downloads of Claude are on the up. (TechCrunch)2 Iranian apps and websites were hacked in the wake of the US-Israeli strikesNews sites and a religious app were co-opted to display anti-military messages. (Reuters)+ They urged personnel to abandon the regime and to liberate the country. (WSJ $)+ Unsurprisingly, X is rife with disinformation about the attacks. (Wired $)+ The campaign has disrupted online delivery orders across the Middle East. (Bloomberg $)3 DeepSeek is poised to release a new AI model this weekThe multimodal V4 is being released ahead of China’s annual parliamentary meetings. (FT $)4 The UK is trialing a social media ban for under-16sHundreds of teens will test overnight digital curfews and screen time limits. (The Guardian)+ What it’s like to attend a phone addiction meeting. (Boston Globe $)
5 Celebrities are winning huge sums playing on this major crypto casino’s slotsIn fact, their lucky wins appear to spike while they’re livestreaming. (Bloomberg $) 6 America is desperate to steal China’s critical mineral leadThe victor essentially controls global computing, aerospace and defense. (Economist $)+ This rare earth metal shows us the future of our planet’s resources. (MIT Technology Review)7 How lasers became the military’s weapon of choiceFrom Ukraine to the US, soldiers are deploying laser guns. But why? (The Atlantic $)+ They’re a key part of America’s arsenal in manning the southern border. (New Yorker $)+ This giant microwave may change the future of war. (MIT Technology Review)
8 How quantum entanglement became big businessIt promises unhackable communication—but is it too good to be true? (New Scientist $)+ Useful quantum computing is inevitable—and increasingly imminent. (MIT Technology Review) 9 The iPod is proving a hit among Gen ZEven though Apple discontinued the music player four years ago. (NYT $)10 Chinese parents are joining matchmaking apps in their drovesIn a bid to marry off their adult children as soon as humanly possible. (Nikkei Asia) Quote of the day “Day to day it just feels untenable…Some managers know this is the case, but executives just keep pointing to some bigger AI picture.” —An anonymous Amazon employee describes the stresses of trying to increase productivity amid the company’s commitment to reducing headcount to the Financial Times.
One more thing The iPad was meant to revolutionize accessibility. What happened?On April 3, 2010, Steve Jobs debuted the iPad. What for most people was basically a more convenient form factor was something far more consequential for non-speakers: a life-­changing revolution in access to a portable, powerful communication device for just a few hundred dollars.But a piece of hardware, however impressively designed and engineered, is only as valuable as what a person can do with it. After the iPad’s release, the flood of new, easy-to-use augmentative and alternative communication apps that users were in desperate need of never came.Today, there are only around half a dozen apps, each retailing for $200 to $300, that ask users to select from menus of crudely drawn icons to produce text and synthesized speech. It’s a depressingly slow pace of development for such an essential human function. Read the full story. —Julie Kim

Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta and Google DeepMind, chanting slogans and waving signs. The march was organized by a coalition of two separate activist groups, Pause AI and Pull the Plug, who billed it as the largest protest of its kind yet. The range of concerns on show covered everything from online slop and abusive images to killer robots and human extinction. One woman wore a large homemade billboard on her head that read “WHO WILL BE WHOSE TOOL?” (with the Os in “TOOL” cut out as eye holes). There were signs that said “Pause before there’s cause” and “EXTINCTION=BAD” and “Demis the Menace” (referring to Demis Hassabis, the CEO of Google DeepMind). Another simply stated: “Stop using AI.” An older man wearing a sandwich board that read “AI? Over my dead body” told me he was concerned about the negative impact of AI on society: “It’s about the dangers of unemployment,” he said. “The devil finds work for idle hands.” This is all familiar stuff. Researchers have been calling out the harms, both real and hypothetical, caused by generative AI—especially models such as OpenAI’s ChatGPT and Google DeepMind’s Gemini—for years. What’s changed is that those concerns are now being taken up by protest movements that can rally significant crowds of people to take to the streets and shout about it.  
The first time I ran into anti-AI protestors was in May 2023, outside a London lecture hall where Sam Altman was speaking. Two or three people stood heckling an audience of hundreds. In June last year Pause AI, a small but international organization set up in 2023 and funded by private donors, drew a crowd of a few dozen people for a protest outside Google DeepMind’s London office. This felt like a significant escalation. “We want people to know Pause AI exists,” Joseph Miller, who heads up Pause AI’s UK branch and co-organized Saturday’s march, told me on a call the day before the protest: “We’ve been growing very rapidly. In fact, we also appear to be on a somewhat exponential path, matching the progress of AI itself.”
Miller is a PhD student at Oxford University, where he studies mechanistic interpretability, a new field of research that involves trying to understand exactly what goes on inside LLMs when they carry out a task. His work has led him to believe that the technology may forever be beyond our control and that this could have catastrophic consequences. It doesn’t have to be a rogue superintelligence, he said. You just needed someone to put AI in charge of nuclear weapons. “The more silly decisions that humanity makes the less powerful the AI has to be before things go bad,” he said. After a week in which the US government tried to force Anthropic to let it use the firm’s LLM Claude for any “legal” military purposes, such fears seem a little less farfetched. Anthropic stood its ground, but OpenAI signed a deal with the DoD instead. (OpenAI declined an invitation to comment on Saturday’s protest.) For Matilda da Rui, a member of Pause AI and co-organizer of the protest, AI is the last problem that humans will face. She thinks the technology will either allow us to solve—once and for all—every other problem that we have, or it will wipe us out and there will be nobody left to have problems any more. “It’s a mystery to me that anyone would really focus on anything else if they actually understood the problem,” she told me. And yet despite that urgency, the atmosphere at the march was pleasant, even fun. There was no sense of anger and little sense that lives—let alone the survival of our species—was at stake. That could be down to the broad coalition of interests and demands that protestors brought with them. A chemistry researcher I spoke to ticked off a litany of complaints, which ranged from the conspiracy-adjacent (that data centers emitted infrasound below the threshold of human hearing, inducing paranoia in people who lived near them) to the reasonable (that the spread of AI slop online was making it hard to find reliable academic sources). The researcher’s solution was to make it illegal for companies to profit from the technology: “If you couldn’t make money from AI, it wouldn’t be such a problem.” Most people I spoke to agreed that technology companies probably wouldn’t take any notice of this kind of protest. “I don’t think that the pressure on companies will ever work,” Maxime Fournes, the global head of Pause AI, told me when I bumped into him at the march: “They are optimized to just not care about this problem.” But Fournes, who worked in the AI industry for 12 years before joining Pause AI, thinks he can make it harder for those companies. “We can slow down the race by creating protection for whistleblowers or showing the public that working in AI is not a sexy job, that actually it’s a terrible job—you can dry up the talent pipeline.”

In general, most protestors hoped to make as many people as possible aware of the issues and to use that groundswell to push for government regulation. The organizers had pitched the march as a social event, encouraging anyone curious about the cause to come along. It seemed to have worked. I met a man who worked in finance who had tagged along with his roommate. I asked why he was there. “Sometimes you don’t have that much to do on a Saturday anyway,” he said. “If you can see the logic of the argument, it sort of makes sense to you, then it’s like ‘Yeah, sure, I’ll come along and see what it’s like.’” He thought the concerns around AI were hard for anyone to fully oppose. It’s not like a pro-Palestine protest, he said, where you’d have people who might disagree with the cause. “With this, I feel like it’s very hard for someone to totally oppose what you’re marching for.” After winding its way through King’s Cross, the march ended in a church hall in Bloomsbury, where tables and chairs had been set up in rows. The protestors wrote their names on stickers, stuck them to their chests and made awkward introductions to their neighbors. They were here to figure out how to save the world. But I had a train to catch and I left them to it. 

AI is often described as a black box, but it’s not just its inner workings that are mysterious. Leading AI companies have kept figures on energy use closely guarded, making it hard to determine its climate impact. In a rigorous investigation, senior AI reporter James O’Donnell and senior climate reporter Casey Crownhart spent six months digging through hundreds of pages of reports, interviewing experts, and crunching the numbers.  The team drilled down into the energy cost of a single prompt, and then zoomed out to build a broader picture illustrating the potential impacts of AI’s current and future energy demand. Their work revealed just how big AI’s energy footprint is, where that energy comes from, and who will pay for it. In the months following the project’s publication, major AI companies including Open AI, Mistral, and Google published details about their models’ energy and water usage.  The 2026 awards will be presented in New York City on May 19. 

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. AI is rewiring how the world’s best Go players think Ten years ago AlphaGo, Google DeepMind’s AI program, stunned the world by defeating the South Korean Go player Lee Sedol.And in the years since, AI has upended the game. It’s overturned centuries-old principles about the best moves and introduced entirely new ones. Players now train to replicate AI’s moves as closely as they can rather than inventing their own, even when the machine’s thinking remains mysterious to them. Meanwhile, AI is democratizing access to training, and more female players are climbing the ranks as a result.Today, it is essentially impossible to compete professionally without using AI. Some say the technology has drained the game of its creativity, while others think there is still room for human invention. Read the full story. —Michelle Kim
MIT Technology Review Narrated: Hackers made death threats against this security researcher. Big mistake.
In April 2024, a mysterious someone using the online handles “Waifu” and “Judische” began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon.As chief research officer at the cyber investigations firm Unit 221B, Nixon had built a career tracking cybercriminals and helping get them arrested. And although she had taken an interest in the Waifu persona in years past for crimes he boasted about committing, he hadn’t been on her radar for a while when the threats began, because she was tracking other targets. Now Nixon resolved to unmask Waifu/Judische and others responsible for the death threats—and take them down for crimes they admitted to committing. 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 Anthropic has refused the Pentagon’s AI demands It’s holding firm on its stance: no mass surveillance of Americans, and no lethal autonomous weapons. (The Verge)+ Anthropic said “virtually no progress” had been made during recent talks. (The Hill)+ Here’s how relations between the US government and the company started to dissolve. (Vox)2 Instagram will alert parents if teens repeatedly search for suicide materialBut campaigners fear the measure could do more harm than good. (BBC)+ Instagram is working on a similar alert feature for its AI tools. (Engadget)+ Poland is weighing up banning under-15s from accessing social media. (Bloomberg $)3 ChatGPT Health regularly fails to recognize medical emergenciesIn more than half of serious cases, it advised users to delay seeking treatment. (The Guardian)+ “Dr. Google” had its issues. Can ChatGPT Health do better? (MIT Technology Review)4 The Islamic State’s online warriors are posting beyond the graveThe group is using AI to resurrect dead leaders and port them to new platforms. (404 Media)5 Vegetarians are at lower risk from five types of cancerIt suggests that avoiding meat could help to avoid certain cancers, including breast and pancreatic. (FT $)+ Interestingly, the same doesn’t apply for vegans. (Bloomberg $)+ RFK Jr. follows a carnivore diet. That doesn’t mean you should. (MIT Technology Review)6 Activists combating online abuse have been barred from AmericaAuthorities accused HateAid of participating in a “global censorship-industrial complex.” (NYT $)+ What it’s like to be banned from the US for fighting online hate. (MIT Technology Review) 7 Russians are looking for missing soldiers on Google MapsThey’re posting reviews pleading for information about missing loved ones. (New Yorker $)+ Google Maps has finally gained approval to operate in South Korea. (FT $)+ It’s hellbent on closing its final few global gaps. (Economist $)

8 Burger King’s new AI assistant will evaluate workers’ friendlinessIt’ll check interactions to make sure they’re saying please and thank you. (The Verge)+ Perplexity’s bossy new AI agent assigns work to fellow agents. (Ars Technica)9 NASA still hasn’t made it back to the moonThe mission has been dogged by delays and issues. (WP $)10 Are you Chinamaxxing yet?Everyone on TikTok is, c’mon. (Insider $) Quote of the day “This is as much of a political fight as a military use issue.” —Steven Feldstein, a senior fellow at the Carnegie Endowment, who researches AI in warfare, explains to the Washington Post why ideological differences are likely to be worsening the rift between Anthropic and the Pentagon. One more thing One city’s fight to solve its sewage problem with sensorsIn the city of South Bend, Indiana, wastewater from people’s kitchens, sinks, washing machines, and toilets flows through 35 neighborhood sewer lines. On good days, just before each line ends, a vertical throttle pipe diverts the sewage into an interceptor tube, which carries it to a treatment plant where solid pollutants and bacteria are filtered out.As in many American cities, those pipes are combined with storm drains, which can fill rivers and lakes with toxic sludge when heavy rains or melted snow overwhelms them, endangering wildlife and drinking water supplies. But city officials have a plan to make its aging sewers significantly smarter. Read the full story.—Andrew Zaleski
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.) + This is a fascinating insight into Jimi Hendrix’s technical guitar wizardry 🎸+ The Romans: their lives really weren’t so different to ours, y’know.+ How the Beatles kicked back and relaxed at home when they weren’t shaping history.+ Disney composer Alan Menken is an undisputed talent.

Burrowed in the alleys of Hongik-dong, a hushed residential neighborhood in eastern Seoul, is a faded stone-tiled building stamped “Korea Baduk Association,” the governing body for professional Go. The game is an ancient one, with sacred stature in South Korea.  But inside the building, rooms once filled with the soft clatter of hands dipping into wooden bowls of stones now echo with mouse clicks. Players hunch over their monitors and replay their matches in an AI program. Others huddle around a Go board and debate the best next move, while coaches report how their choices stack up against the AI’s. Some sit in silence, watching AI programs play against each other.  Ten years ago AlphaGo, Google DeepMind’s AI program, stunned the world by defeating the South Korean Go player Lee Sedol. And in the years since, AI has upended the game. It’s overturned centuries-old principles about the best moves and introduced entirely new ones. Players now train to replicate AI’s moves as closely as they can rather than inventing their own, even when the machine’s thinking remains mysterious to them. Today, it is essentially impossible to compete professionally without using AI. Some say the technology has drained the game of its creativity, while others think there is still room for human invention. Meanwhile, AI is democratizing access to training, and more female players are climbing the ranks as a result.  For Shin Jin-seo, the top-ranked Go player in the world, AI is an invaluable training partner. Every morning, he sits at his computer and opens a program called KataGo. Nicknamed “Shintelligence” for how closely his moves mimic AI’s, he traces the glowing “blue spot” that represents the program’s suggestion for the best next move, rearranging the stones on the digital grid to try to understand the machine’s thinking. “I constantly think about why AI chose a move,” he says.
When training for a match, Shin spends most of his waking hours poring over KataGo. “It’s almost like an ascetic practice,” he says. According to a study in 2022 by the Korean Baduk League, Shin’s moves match AI’s 37.5% of the time, well above the 28.5% average the study found among all players. “My game has changed a lot,” says Shin, “because I have to follow the directions suggested by AI to some extent.” The Korea Baduk Association says it has reached out to Google DeepMind in the hopes of arranging a match between Shin and AlphaGo, to commemorate the 10th anniversary of its victory over Lee. A spokesperson for Google DeepMind said the company could not provide information at this time. But if a new match does happen, Shin, who has trained on more advanced AI programs, is optimistic that he’d win. “AlphaGo still had some flaws then, so I think I could beat it if I target those weaknesses,” he says.
AI rewrites the Go playbook Go is an abstract strategy board game invented in China more than 2,500 years ago. Two players take turns placing black and white stones on a 19×19 grid, aiming to conquer territory by surrounding their opponent’s stones. It’s a game of striking mathematical complexity. The number of possible board configurations—roughly 10170—dwarfs the number of atoms in the universe. If chess is a battle, Go is a war. You suffocate your enemy in one corner while fending off an invasion in another. To train AI to play Go, a vast trove of human Go moves are fed into a neural network, a computing system that mimics the web of neurons in the human brain. AlphaGo, which was later christened AlphaGo Lee after its victory over Lee Sedol, was trained on 30 million Go moves and refined by playing millions of games against itself. In 2017, its successor, AlphaGo Zero, picked up Go from scratch. Without studying any human games, it learned by playing against itself, with moves based only on the rules of the game. The blank-slate approach proved more powerful, unconstrained by the limits of human knowledge. After three days of training, it beat AlphaGo Lee 100 games to zero.  Google DeepMind retired AlphaGo that same year. But then a wave of open-source models inspired by AlphaGo Zero emerged. Today, KataGo is the program most widely used by professional Go players in South Korea. It’s faster and sharper than AlphaGo. It’s learned to predict not just who will win, but also who owns each point on the board at any given moment. While AlphaGo Zero pieced together its understanding of the board by looking at small sections, KataGo learned to read the whole board, developing better judgment for long-term strategies. Instead of just learning how to win, it learned to maximize its score. The software has reshaped how people play. For hundreds of years, professional Go players have navigated the game’s astronomical complexity by developing heuristics that replaced brute calculation. Elegant opening strategies imposed abstract order on the empty grid. Invading corners early was a bad bargain. Each generation of Go players added new principles to the canon.  But “AI has changed everything,” says Park Jeong-sang, a South Korean Go commentator. “Fundamental moves that were once considered common sense aren’t played at all today, and techniques that didn’t exist before have become popular.”  The starkest shift has been in opening moves. Go starts on a blank grid, and the first 50 moves were canvases for abstract thinking and creativity, where players etched their personalities and philosophies. Lee Sedol fashioned provocative moves that invited chaos. Ke Jie, a Chinese player who was defeated by AlphaGo Master in 2017, dazzled with agile, imaginative moves. Now, players memorize the same strain of efficient, calculated opening moves suggested by AI. The crux of the game has shifted to the middle moves, where raw calculation matters more than creativity. Training with AI has led to a homogenization of playing styles. Ke Jie has lamented the strain of watching the same opening moves recycled endlessly. “I feel the exact same way as the fans watching. It’s very tiring and painful to watch,” he told a Chinese news outlet in 2021. Fans revel when a player breaks from the script with offbeat moves, but those moments have become rarer. Over a third of moves by the top Go players replicate AI’s recommendations, according to a study in 2023. The first 50 moves of each game are often identical to what AI suggests, many players say.  “Go has become a mind sport,” says Lee Sedol, who retired three years after his 2016 defeat to AlphaGo. “Before AI, we sought something greater. I learned Go as an art,” he says. “But if you copy your moves from an answer key, that’s no longer art.” 

Playing Go is no longer about charting new frontiers, some players say, but about following the dictates of a superhuman oracle. “I used to inspire fans by advancing the techniques of Go and presenting a new paradigm,” says Lee. “My reason for playing Go has vanished.” A mysterious mind The players who have stayed in the game are trying to reinvent their craft. But it can be hard to discern what the new principles are. Disarmingly slight and formidably calm, Kim Chae-young, one of the top female Go players in the world, grew up learning the game from her father, who was also a professional Go player. But when AI began to reshape the game, she found herself starting over. “I needed time to abandon everything I had learned before,” says Kim who shared her screen with me as she pointed her cursor to the blue spots suggested by KataGo. “The intuition I had built up over the years turned out to be wrong.”  As she leaned close to her monitor, her blinking screen showed the winning probabilities of each move, with no explanations. Even top players like Kim and Shin don’t understand all of AI’s moves. “It seems like it’s thinking in a higher dimension,” she says. When she tries to learn from AI, she adds, “it’s less about rationally thinking through each move, but more about developing a gut feeling—an intuition.” Researchers are trying to discover the superhuman knowledge encoded in game-playing AI programs so that humans can learn it too. In 2024, researchers at Google DeepMind extracted new chess concepts from AlphaZero, a generalized version of AlphaGo Zero that can also play chess, and taught them to chess grandmasters using chess puzzles. The Go concepts that players have picked up from AI systems so far are “probably only a small portion of what you could potentially learn,” says Nicholas Tomlin, a computer scientist at Toyota Technological Institute at Chicago, who coauthored a study probing Go concepts encoded in AlphaGo Zero. But extracting those lessons remains a struggle. “Top-tier players haven’t yet been able to deduce the general principles behind AI moves,” says Nam Chi-hyung, a Go professor at Myongji University. Although they can emulate AI’s moves, they have yet to glean a new paradigm for the game because its reasoning is a black box, she says. Go may be in an epistemic limbo.  Even if AI is an opaque teacher, it’s a democratic one. It has supercharged training for female Go players, who have long been underdogs of the game. For decades, training meant studying under top male players, and the most competitive matches took place in male circles that were difficult for women to break into, says Nam. “Female players never had access to that experience,” she says. “But now they can study with AI, which has made their training environment much more favorable.” More broadly, AI has narrowed the gap between players by helping everyone perfect their opening moves. Female players have climbed the ranks over the last few years as a result. In 2022, Choi Jeong, then the top female player in the world, became the first woman to reach the finals of a major international Go tournament. Dubbed “Girl Wrestler” for her fierce, combative style of play, she took on Shin. She lost, but the match broke new ground for women in Go. In 2024, Kim made headlines for winning the Korean Go League’s postseason playoffs. She was the only female player in the tournament. 
Training with AI has given Kim newfound confidence. Analyzing male players’ moves with AI has shattered their veneer of infallibility. “Before, I couldn’t gauge just how strong top male players were—they felt invincible. Now, I know that they make mistakes, and their moves aren’t always brilliant,” she says. “AI broke the psychological barrier.” Go players find a new identity Although AI has mastered Go far better than any player, fans continue to prefer watching people play. “A Go game between AI programs is not very fun for fans to watch,” says Park, the Go commentator. Such matches are too complex for fans to follow, too flawless to be thrilling, he says. 
Players can mimic AI’s opening moves, but in the middle game—where the board branches into too many possibilities to memorize—their own judgment takes over. Fans revel in watching players make mistakes and mount comebacks, exuding personality in every stone on the board. Shin’s playing style is combative but marked by machinelike poise. Kim deftly navigates  the most chaotic positions on the board.  “In Go, every move is a choice you make, and your opponent responds with a choice of their own,” says Kim Dae-hui, 27, a Go fan and amateur player. “Watching that process unfold is fun.” With fans like Kim still watching, Shin finds meaning in his game. “I can play a kind of Go that tells a story that only a human can,” he says.  After his retirement, Lee searched for a new job where he could have an edge as a human. He started making board games, giving speeches, and teaching students at a university. “I’m looking for a new domain that I can enjoy and excel at,” he says. But lately, he feels more hopeful for the game he left behind. “It’s every Go player’s dream to play a masterpiece game,” he says—a game of technical brilliance, with no mistakes, fought to a razor’s edge between evenly matched players. “It’s like a mirage,” Lee says, chuckling. “Maybe AI can help us play a masterpiece.”  Shin hopes he can do that. To Shin, AI is a teacher, a companion, and a North Star. “I may be one of the strongest human players, but with AI around, I can’t be so arrogant,” he says. “AI gives me a reason to keep improving.”