AI is rewiring how the world’s best Go players think

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AI is rewiring how the world’s best Go players think

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.”

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.”

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Finder Energy Holdings Ltd. received regulatory approval for a development area covering the Kuda Tasi and Jahal oil fields offshore Timor‑Leste, enabling progression toward field development. Autoridade Nacional do Petróleo (ANP) approved an 88‑sq km development area over the Kuda Tasi and Jahal oil fields (KTJ Project) within PSC 19‑11 offshore Timor‑Leste, representing the first stage of the regulatory approvals process for the project. The declaration of the development area is a precursor to the field development plan (FDP), which Finder is currently preparing for submission to ANP in second‑quarter 2026. Upon approval of the FDP, the development area would secure tenure for up to 25 years or until production ceases, allowing Finder to conduct development and production operations within the area, subject to applicable regulatory approvals and conditions. The company said its upside strategy centers on the potential for the Petrojarl I FPSO to serve as a central processing and export hub for future tiebacks of surrounding discoveries, contingent on successful appraisal and/or exploration activities within PSC 19‑11. Alternatively, longer tie‑back distances could be accommodated through a secondary standalone development in the southern portion of the PSC. Finder is continuing technical evaluation of appraisal and exploration opportunities to generate drilling targets. PSC 19‑11 lies within the Laminaria High oil province of Timor‑Leste. The KTJ Project contains an estimated 25 million bbl of gross 2C contingent resources, with identified upside of an additional 23 million bbl gross 2C contingent resources and 116 million bbl gross 2U prospective resources. Finder operates PSC 19‑11 with a 66% working interest.

Newly formed Polar LNG aims to develop nearshore LNG project on Alaska’s North Slope

Polar Train LNG LLC, a newly launched company aiming to build an LNG plant (Polar LNG) on Alaska’s North Slope, has appointed Joel Riddle as president and chief executive officer. “Alaska’s North Slope holds one of the most significant undeveloped natural gas resources in the world,” said Riddle, adding “Polar LNG is uniquely positioned to bring this resource online—delivering reliable energy for Alaska and a strategic supply for the United States… and provides trusted energy to our allies.” In a release Mar. 31, the company said it is advancing a nearshore project at Prudhoe Bay, citing “one of the shortest LNG shipping routes from North America to key Asian markets, approximately 3,600 miles to Japan compared to over 10,000 miles from the US Gulf Coast.” The company is aiming for first LNG from the 7-million tonnes/year plant—to be developed nearshore with modular infrastructure—in 2029-2030 at a cost of $8–9 billion. According to Polar LNG, natural gas would be sourced from existing infrastructure at Prudhoe Bay and transported via a short pipeline to a nearshore plant. There, a modular gravity-based structure would process and liquefy the gas. LNG would then be loaded onto specialized ice-class carriers for year-round export. The company is exploring potential repurposing of sanctioned equipment built for Russia’s Arctic LNG 2 project and is seeking permission from the US govenment to acquire parts impacted by the sanctions, according to reports. Before joining Polar LNG, Riddle served as managing director and chief executive officer of Tamboran Resources Ltd.

Asia bears brunt of energy shock as Middle East war disrupts liquid flows

Asia is facing a dual energy crisis marked by both soaring prices and physical supply disruptions as escalating war in the Middle East constrains flows through the Strait of Hormuz, according to a new report by Morningstar DBRS. The report highlights that roughly one-fifth of global crude oil and LNG supply has been affected by disruptions at the critical chokepoint, with Asia absorbing the majority of the impact due to its heavy dependence on imported hydrocarbons. About 83% of oil and LNG shipments passing through Hormuz are destined for Asian markets, amplifying the region’s exposure. Asia’s structural reliance on Middle Eastern energy imports has intensified the shock. Countries such as Japan and South Korea import nearly all of their energy needs, while China and India depend heavily on foreign supplies, much of it sourced from the Gulf. This dependence, combined with limited alternative shipping routes, has turned what initially appeared to be a price-driven shock into a broader supply and logistics crisis. Governments across the region have begun implementing emergency measures, including fuel rationing, price controls, and strategic reserve releases, to manage shortages and rising costs. Policy responses vary In North Asia, policymakers are leveraging stronger buffers. Japan has tapped strategic oil reserves and introduced subsidies to cushion consumers, while South Korea is relying on LNG stockpiles and fuel-switching capabilities. China has deployed administrative controls to stabilize domestic fuel prices and restrict refined product exports. By contrast, parts of South and Southeast Asia are more vulnerable. India has introduced tax relief and prioritized gas allocation, while countries such as the Philippines and Vietnam have declared energy emergencies and rolled out conservation measures. Several ASEAN (the Association of Southeast Asian Nations) economies have even implemented partial work-from-home policies to curb fuel consumption. Broader economic spillovers intensify Beyond energy markets, the disruption

Nscale Expands AI Factory Strategy With Power, Platform, and Scale

Nscale has moved quickly from startup to serious contender in the race to build infrastructure for the AI era. Founded in 2024, the company has positioned itself as a vertically integrated “neocloud” operator, combining data center development, GPU fleet ownership, and a software stack designed to deliver large-scale AI compute. That model has helped it attract backing from investors including Nvidia, and in early March 2026 the company raised another $2 billion at a reported $14.6 billion valuation. Reuters has described Nscale’s approach as owning and operating its own data centers, GPUs, and software stack to support major customers including Microsoft and OpenAI. What makes Nscale especially relevant now is that it is no longer content to operate as a cloud intermediary or capacity provider. Over the past year, the company has increasingly framed itself as an AI hyperscaler and AI factory builder, seeking to combine land, power, data center shells, GPU procurement, customer offtake, and software services into a single integrated platform. Its acquisition of American Intelligence & Power Corporation, or AIPCorp, is the clearest signal yet of that shift, bringing energy infrastructure directly into the center of Nscale’s business model. The AIPCorp transaction is significant because it gives Nscale more than additional development capacity. The company said the deal includes the Monarch Compute Campus in Mason County, West Virginia, a site of up to 2,250 acres with a state-certified AI microgrid and a power runway it says can scale beyond 8 gigawatts. Nscale also said the acquisition establishes a new division, Nscale Energy & Power, headquartered in Houston, extending its platform further into power development. That positioning reflects a broader shift in the AI infrastructure market. The central bottleneck is no longer simply access to GPUs. It is the ability to assemble power, cooling, land, permits, data center

Google Research touts memory-compression breakthrough for AI processing

The last time the market witnessed a shakeup like this was China’s DeepSeek, but doubts emerged quickly about its efficacy. Developers found DeepSeek’s efficiency gains required deep architectural decisions that had to be built in from the start. TurboQuant requires no retraining or fine-tuning. You just drop it straight into existing inference pipelines, at least in theory. If it works in production systems with no retrofitting, then data center operators will get tremendous performance gains on existing hardware. Data center operators won’t have to throw hardware at the performance problem. However, analysts urge caution before jumping to conclusions. “This is a research breakthrough, not a shipping product,” said Alex Cordovil, research director for physical infrastructure at The Dell’Oro Group. “There’s often a meaningful gap between a published paper and real-world inference workloads.” Also, Dell’Oro notes that efficiency gains in AI compute tend to get consumed by more demand, known as the Jevons paradox. “Any freed-up capacity would likely be absorbed by frontier models expanding their capabilities rather than reducing their hardware footprint.” Jim Handy, president of Objective Analysis, agrees on that second part. “Hyperscalers won’t cut their spending – they’ll just spend the same amount and get more bang for their buck,” he said. “Data centers aren’t looking to reach a certain performance level and subsequently stop spending on AI. They’re looking to out-spend each other to gain market dominance. This won’t change that.” Google plans to present a paper outlining TurboQuant at the ICLR conference in Rio de Janeiro running from April 23 through April 27.

Amazon Middle East datacenter suffers second drone hit as Iran steps up attacks

Amazon was contacted for comment on the latest Bahrain drone incident, but said it had nothing to add beyond the statement in its current advisory. Denial of infrastructure Doing the damage is the Shaheed 136, a small and unsophisticated drone designed to overwhelm defenders with numbers. If only one in twenty reaches its target, the price-performance still exceeds that of more expensive systems. When aimed at critical infrastructure such as datacenters, the effect is also psychological; the threat of an attack on its own can be enough to make it difficult for organizations to continue using an at-risk facility. Iran’s targeting of the Bahrain datacenter is unlikely to be random. Amazon opened its ME-SOUTH-1 AWS presence in 2019, and it is still believed to be the company’s largest site in the Middle East. Earlier this week, the Islamic Revolutionary Guard Corps (IRGC) Telegram channel explicitly threatened to target at least 18 US companies operating in the region, including Microsoft, Google, Nvidia, and Apple. This follows similar threats to an even longer list of US companies made on the IRGC-affiliated Tasnim News Agency in recent weeks. That strategy doesn’t bode well for US companies that have made large investments in Middle Eastern datacenter infrastructure in recent years, drawn by the growing wealth and influence of countries in the region. This includes Amazon, which has announced plans to build a $5.3 billion datacenter in Saudi Arabia, due to become available in 2026. If this is now under threat, whether by warfare or the hypothetical possibility of attack, that will create uncertainty.

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Power Applications Engineer Pittsburgh, PA This position is also available in: Denver, CO and Andrews, SC. Our client is a leading provider and manufacturer of industrial electrical power equipment used in industrial applications for mission critical operations. They help their customers save money by reducing energy and operating costs and provide solutions for modernizing their customer’s existing electrical infrastructure. This company provides cooling solutions to many of the world’s largest organizations and government facilities and enterprise clients, colocation providers and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Ashburn, VA This traveling position is also available in: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive

No joke: data centers are warming the planet

The researchers also made use of a database provided by the International Energy Agency (IEA) that the authors pointed out contains more than 11,000 locations worldwide, of which 8,472 have been detected to dwell outside of highly dense urban areas. The latter locations were then used to “quantify the effect of data centers on the environment in terms of the LST gradient that could be measured on the areas surrounding each data center.” Asking the wrong question Asked if AI data centers are really causing local warming, or if this phenomenon is overstated, Sanchit Vir Gogia, chief analyst at Greyhound Research, said, “the signal is real, but the industry is asking the wrong question. The research shows a consistent rise in land surface temperature of around 2°C following the establishment of large data centre facilities.” The debate, however, “has quickly shifted to causality: whether this is driven by operational heat from compute, or by land transformation during construction. That distinction matters scientifically, but it does not change the strategic implication.” Land surface temperature, said Gogia, is not the same as air temperature, and that gap will be used to challenge the findings. “But dismissing the signal on that basis would be a mistake,” he noted. “Data centers concentrate energy use, replace natural surfaces with heat-retaining materials, and continuously reject heat into the environment. Those are known drivers of thermal change.” He added, “the uncomfortable truth is this: Even if the exact mechanism is debated, the outcome aligns with first principles. Infrastructure at this scale alters its surroundings. The industry does not yet have a clean way to separate construction impact from operational impact, and that ambiguity makes the risk harder to model, not easier. This is not overstated, it is under-interpreted.” Location strategy must change But will the findings change

Schneider Electric Maps the AI Data Center’s Next Design Era

The coming shift to higher-voltage DC That internal power challenge led Simonelli to one of the most consequential architectural topics in the interview: the likely transition toward higher-voltage DC distribution at very high rack densities. He framed it pragmatically. At current density levels, the industry knows how to get power into racks at 200 or 300 kilowatts. But as densities rise toward 400 kilowatts and beyond, conventional AC approaches start to run into physical limits. Too much cable, too much copper, too much conversion equipment, and too much space consumed by power infrastructure rather than GPUs. At that point, he said, higher-voltage DC becomes attractive not for philosophical reasons, but because it reduces current, shrinks conductor size, saves space, and leaves more room for revenue-generating compute. “It is again a paradigm shift,” Simonelli said of DC power at these densities. “But it won’t be everywhere.” That is probably right. The transition will not be universal, and the exact thresholds will evolve. But his underlying point is powerful. As rack densities climb, electrical architecture starts to matter not only for efficiency and reliability, but for physical space allocation inside the rack. Put differently, power distribution becomes a compute-enablement issue. Distance between accelerators matters, too. The closer GPUs and TPUs can be kept together, the better they perform. If power infrastructure can be compacted, more of the rack can be devoted to dense compute, improving the economics and performance of the system. That is a strong example of how AI is collapsing traditional boundaries between facility engineering and compute architecture. The two are no longer cleanly separable. Gas now, renewables over time On onsite power, Simonelli was refreshingly direct. If the goal is dispatchable onsite generation at the scale now being contemplated for AI facilities, he said, “there really isn’t an alternative

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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