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China built hundreds of AI data centers to catch the AI boom. Now many stand unused.

A year or so ago, Xiao Li was seeing floods of Nvidia chip deals on WeChat. A real estate contractor turned data center project manager, he had pivoted to AI infrastructure in 2023, drawn by the promise of China’s AI craze.  At that time, traders in his circle bragged about securing shipments of high-performing Nvidia GPUs that were subject to US export restrictions. Many were smuggled through overseas channels to Shenzhen. At the height of the demand, a single Nvidia H100 chip, a kind that is essential to training AI models, could sell for up to 200,000 yuan ($28,000) on the black market.  Now, his WeChat feed and industry group chats tell a different story. Traders are more discreet in their dealings, and prices have come back down to earth. Meanwhile, two data center projects Li is familiar with are struggling to secure further funding from investors who anticipate poor returns, forcing project leads to sell off surplus GPUs. “It seems like everyone is selling, but few are buying,” he says. Just months ago, a boom in data center construction was at its height, fueled by both government and private investors. However, many newly built facilities are now sitting empty. According to people on the ground who spoke to MIT Technology Review—including contractors, an executive at a GPU server company, and project managers—most of the companies running these data centers are struggling to stay afloat. The local Chinese outlets Jiazi Guangnian and 36Kr report that up to 80% of China’s newly built computing resources remain unused. Renting out GPUs to companies that need them for training AI models—the main business model for the new wave of data centers—was once seen as a sure bet. But with the rise of DeepSeek and a sudden change in the economics around AI, the industry is faltering. “The growing pain China’s AI industry is going through is largely a result of inexperienced players—corporations and local governments—jumping on the hype train, building facilities that aren’t optimal for today’s need,” says Jimmy Goodrich, senior advisor for technology at the RAND Corporation.  The upshot is that projects are failing, energy is being wasted, and data centers have become “distressed assets” whose investors are keen to unload them at below-market rates. The situation may eventually prompt government intervention, he says: “The Chinese government is likely to step in, take over, and hand them off to more capable operators.” A chaotic building boom When ChatGPT exploded onto the scene in late 2022, the response in China was swift. The central government designated AI infrastructure as a national priority, urging local governments to accelerate the development of so-called smart computing centers—a term coined to describe AI-focused data centers. In 2023 and 2024, over 500 new data center projects were announced everywhere from Inner Mongolia to Guangdong, according to KZ Consulting, a market research firm. According to the China Communications Industry Association Data Center Committee, a state-affiliated industry association, at least 150 of the newly built data centers were finished and running by the end of 2024. State-owned enterprises, publicly traded firms, and state-affiliated funds lined up to invest in them, hoping to position themselves as AI front-runners. Local governments heavily promoted them in the hope they’d stimulate the economy and establish their region as a key AI hub.  However, as these costly construction projects continue, the Chinese frenzy over large language models is losing momentum. In 2024 alone, over 144 companies registered with the Cyberspace Administration of China—the country’s central internet regulator—to develop their own LLMs. Yet according to the Economic Observer, a Chinese publication, only about 10% of those companies were still actively investing in large-scale model training by the end of the year. China’s political system is highly centralized, with local government officials typically moving up the ranks through regional appointments. As a result, many local leaders prioritize short-term economic projects that demonstrate quick results—often to gain favor with higher-ups—rather than long-term development. Large, high-profile infrastructure projects have long been a tool for local officials to boost their political careers. The post-pandemic economic downturn only intensified this dynamic. With China’s real estate sector—once the backbone of local economies—slumping for the first time in decades, officials scrambled to find alternative growth drivers. In the meantime, the country’s once high-flying internet industry was also entering a period of stagnation. In this vacuum, AI infrastructure became the new stimulus of choice. “AI felt like a shot of adrenaline,” says Li. “A lot of money that used to flow into real estate is now going into AI data centers.” By 2023, major corporations—many of them with little prior experience in AI—began partnering with local governments to capitalize on the trend. Some saw AI infrastructure as a way to justify business expansion or boost stock prices, says Fang Cunbao, a data center project manager based in Beijing. Among them were companies like Lotus, an MSG manufacturer, and Jinlun Technology, a textile firm—hardly the names one would associate with cutting-edge AI technology. This gold-rush approach meant that the push to build AI data centers was largely driven from the top down, often with little regard for actual demand or technical feasibility, say Fang, Li, and multiple on-the-ground sources, who asked to speak anonymously for fear of political repercussions. Many projects were led by executives and investors with limited expertise in AI infrastructure, they say. In the rush to keep up, many were constructed hastily and fell short of industry standards.  “Putting all these large clusters of chips together is a very difficult exercise, and there are very few companies or individuals who know how to do it at scale,” says Goodrich. “This is all really state-of-the-art computer engineering. I’d be surprised if most of these smaller players know how to do it. A lot of the freshly built data centers are quickly strung together and don’t offer the stability that a company like DeepSeek would want.” To make matters worse, project leaders often relied on middlemen and brokers—some of whom exaggerated demand forecasts or manipulated procurement processes to pocket government subsidies, sources say.  By the end of 2024, the excitement that once surrounded China’s data center boom was  curdling into disappointment. The reason is simple: GPU rental is no longer a particularly  lucrative business. The DeepSeek reckoning The business model of data centers is in theory straightforward: They make money by renting out GPU clusters to companies that need computing capacity for AI training. In reality, however, securing clients is proving difficult. Only a few top tech companies in China are now drawing heavily on computing power to train their AI models. Many smaller players have been giving up on pretraining their models or otherwise shifting their strategy since the rise of DeepSeek, which broke the internet with R1, its open-source reasoning model that matches the performance of ChatGPT o1 but was built at a fraction of its cost.  “DeepSeek is a moment of reckoning for the Chinese AI industry. The burning question shifted from ‘Who can make the best large language model?’ to ‘Who can use them better?’” says Hangcheng Cao, an assistant professor of information systems at Emory University.  The rise of reasoning models like DeepSeek’s R1 and OpenAI’s ChatGPT o1 and o3 has also changed what businesses want from a data center. With this technology, most of the computing needs come from conducting step-by-step logical deductions in response to users’ queries, not from the process of training and creating the model in the first place. This reasoning process often yields better results but takes significantly more time. As a result, hardware with low latency (the time it takes for data to pass from one point on a network to another) is paramount. Data centers need to be located near major tech hubs to minimize transmission delays and ensure access to highly skilled operations and maintenance staff.  This change means many data centers built in central, western, and rural China—where electricity and land are cheaper—are losing their allure to AI companies. In Zhengzhou, a city in Li’s home province of Henan, a newly built data center is even distributing free computing vouchers to local tech firms but still struggles to attract clients.  Additionally, a lot of the new data centers that have sprung up in recent years were optimized for pretraining workloads—large, sustained computations run on massive data sets—rather than for inference, the process of running trained reasoning models to respond to user inputs in real time. Inference-friendly hardware differs from what’s traditionally used for large-scale AI training.  GPUs like Nvidia H100 and A100 are designed for massive data processing, prioritizing speed and memory capacity. But as AI moves toward real-time reasoning, the industry seeks chips that are more efficient, responsive, and cost-effective. Even a minor miscalculation in infrastructure needs can render a data center suboptimal for the tasks clients require. In these circumstances, the GPU rental price has dropped to an all-time low. A recent report from the Chinese media outlet Zhineng Yongxian said that an Nvidia H100 server configured with eight GPUs now rents for 75,000 yuan per month, down from highs of around 180,000. Some data centers would rather leave their facilities sitting empty than run the risk of losing even more money because they are so costly to run, says Fan: “The revenue from having a tiny part of the data center running simply wouldn’t cover the electricity and maintenance cost.” “It’s paradoxical—China faces the highest acquisition costs for Nvidia chips, yet GPU leasing prices are extraordinarily low,” Li says. There’s an oversupply of computational power, especially in central and west China, but at the same time, there’s a shortage of cutting-edge chips.  However, not all brokers were looking to make money from data centers in the first place. Instead, many were interested in gaming government benefits all along. Some operators exploit the sector for subsidized green electricity, obtaining permits to generate and sell power, according to Fang and some Chinese media reports. Instead of using the energy for AI workloads, they resell it back to the grid at a premium. In other cases, companies acquire land for data center development to qualify for state-backed loans and credits, leaving facilities unused while still benefiting from state funding, according to the local media outlet Jiazi Guangnian. “Towards the end of 2024, no clear-headed contractor and broker in the market would still go into the business expecting direct profitability,” says Fang. “Everyone I met is leveraging the data center deal for something else the government could offer.” A necessary evil Despite the underutilization of data centers, China’s central government is still throwing its weight behind a push for AI infrastructure. In early 2025, it convened an AI industry symposium, emphasizing the importance of self-reliance in this technology.  Major Chinese tech companies are taking note, making investments aligning with this national priority. Alibaba Group announced plans to invest over $50 billion in cloud computing and AI hardware infrastructure over the next three years, while ByteDance plans to invest around $20 billion in GPUs and data centers. In the meantime, companies in the US are doing likewise. Major tech firms including OpenAI, Softbank, and Oracle have teamed up to commit to the Stargate initiative, which plans to invest up to $500 billion over the next four years to build advanced data centers and computing infrastructure. ​Given the AI competition between the two countries, experts say that China is unlikely to scale back its efforts. “If generative AI is going to be the killer technology, infrastructure is going to be the determinant of success,”  says Goodrich, the tech policy advisor to RAND. “The Chinese central government will likely see [underused data centers] as a necessary evil to develop an important capability, a growing pain of sorts. You have the failed projects and distressed assets, and the state will consolidate and clean it up. They see the end, not the means,” Goodrich says. Demand remains strong for Nvidia chips, and especially the H20 chip, which was custom-designed for the Chinese market. One industry source, who requested not to be identified under his company policy, confirmed that the H20, a lighter, faster model optimized for AI inference, is currently the most popular Nvidia chip, followed by the H100, which continues to flow steadily into China even though sales are officially restricted by US sanctions. Some of the new demand is driven by companies deploying their own versions of DeepSeek’s open-source models. For now, many data centers in China sit in limbo—built for a future that has yet to arrive. Whether they will find a second life remains uncertain. For Fang Cunbao, DeepSeek’s success has become a moment of reckoning, casting doubt on the assumption that an endless expansion of AI infrastructure guarantees progress. That’s just a myth, he now realizes. At the start of this year, Fang decided to quit the data center industry altogether. “The market is too chaotic. The early adopters profited, but now it’s just people chasing policy loopholes,” he says. He’s decided to go into AI education next.  “What stands between now and a future where AI is actually everywhere,” he says, “is not infrastructure anymore, but solid plans to deploy the technology.” 

A year or so ago, Xiao Li was seeing floods of Nvidia chip deals on WeChat. A real estate contractor turned data center project manager, he had pivoted to AI infrastructure in 2023, drawn by the promise of China’s AI craze. 

At that time, traders in his circle bragged about securing shipments of high-performing Nvidia GPUs that were subject to US export restrictions. Many were smuggled through overseas channels to Shenzhen. At the height of the demand, a single Nvidia H100 chip, a kind that is essential to training AI models, could sell for up to 200,000 yuan ($28,000) on the black market. 

Now, his WeChat feed and industry group chats tell a different story. Traders are more discreet in their dealings, and prices have come back down to earth. Meanwhile, two data center projects Li is familiar with are struggling to secure further funding from investors who anticipate poor returns, forcing project leads to sell off surplus GPUs. “It seems like everyone is selling, but few are buying,” he says.

Just months ago, a boom in data center construction was at its height, fueled by both government and private investors. However, many newly built facilities are now sitting empty. According to people on the ground who spoke to MIT Technology Review—including contractors, an executive at a GPU server company, and project managers—most of the companies running these data centers are struggling to stay afloat. The local Chinese outlets Jiazi Guangnian and 36Kr report that up to 80% of China’s newly built computing resources remain unused.

Renting out GPUs to companies that need them for training AI models—the main business model for the new wave of data centers—was once seen as a sure bet. But with the rise of DeepSeek and a sudden change in the economics around AI, the industry is faltering.

“The growing pain China’s AI industry is going through is largely a result of inexperienced players—corporations and local governments—jumping on the hype train, building facilities that aren’t optimal for today’s need,” says Jimmy Goodrich, senior advisor for technology at the RAND Corporation. 

The upshot is that projects are failing, energy is being wasted, and data centers have become “distressed assets” whose investors are keen to unload them at below-market rates. The situation may eventually prompt government intervention, he says: “The Chinese government is likely to step in, take over, and hand them off to more capable operators.”

A chaotic building boom

When ChatGPT exploded onto the scene in late 2022, the response in China was swift. The central government designated AI infrastructure as a national priority, urging local governments to accelerate the development of so-called smart computing centers—a term coined to describe AI-focused data centers.

In 2023 and 2024, over 500 new data center projects were announced everywhere from Inner Mongolia to Guangdong, according to KZ Consulting, a market research firm. According to the China Communications Industry Association Data Center Committee, a state-affiliated industry association, at least 150 of the newly built data centers were finished and running by the end of 2024. State-owned enterprises, publicly traded firms, and state-affiliated funds lined up to invest in them, hoping to position themselves as AI front-runners. Local governments heavily promoted them in the hope they’d stimulate the economy and establish their region as a key AI hub. 

However, as these costly construction projects continue, the Chinese frenzy over large language models is losing momentum. In 2024 alone, over 144 companies registered with the Cyberspace Administration of China—the country’s central internet regulator—to develop their own LLMs. Yet according to the Economic Observer, a Chinese publication, only about 10% of those companies were still actively investing in large-scale model training by the end of the year.

China’s political system is highly centralized, with local government officials typically moving up the ranks through regional appointments. As a result, many local leaders prioritize short-term economic projects that demonstrate quick results—often to gain favor with higher-ups—rather than long-term development. Large, high-profile infrastructure projects have long been a tool for local officials to boost their political careers.

The post-pandemic economic downturn only intensified this dynamic. With China’s real estate sector—once the backbone of local economies—slumping for the first time in decades, officials scrambled to find alternative growth drivers. In the meantime, the country’s once high-flying internet industry was also entering a period of stagnation. In this vacuum, AI infrastructure became the new stimulus of choice.

“AI felt like a shot of adrenaline,” says Li. “A lot of money that used to flow into real estate is now going into AI data centers.”

By 2023, major corporations—many of them with little prior experience in AI—began partnering with local governments to capitalize on the trend. Some saw AI infrastructure as a way to justify business expansion or boost stock prices, says Fang Cunbao, a data center project manager based in Beijing. Among them were companies like Lotus, an MSG manufacturer, and Jinlun Technology, a textile firm—hardly the names one would associate with cutting-edge AI technology.

This gold-rush approach meant that the push to build AI data centers was largely driven from the top down, often with little regard for actual demand or technical feasibility, say Fang, Li, and multiple on-the-ground sources, who asked to speak anonymously for fear of political repercussions. Many projects were led by executives and investors with limited expertise in AI infrastructure, they say. In the rush to keep up, many were constructed hastily and fell short of industry standards. 

“Putting all these large clusters of chips together is a very difficult exercise, and there are very few companies or individuals who know how to do it at scale,” says Goodrich. “This is all really state-of-the-art computer engineering. I’d be surprised if most of these smaller players know how to do it. A lot of the freshly built data centers are quickly strung together and don’t offer the stability that a company like DeepSeek would want.”

To make matters worse, project leaders often relied on middlemen and brokers—some of whom exaggerated demand forecasts or manipulated procurement processes to pocket government subsidies, sources say. 

By the end of 2024, the excitement that once surrounded China’s data center boom was  curdling into disappointment. The reason is simple: GPU rental is no longer a particularly  lucrative business.

The DeepSeek reckoning

The business model of data centers is in theory straightforward: They make money by renting out GPU clusters to companies that need computing capacity for AI training. In reality, however, securing clients is proving difficult. Only a few top tech companies in China are now drawing heavily on computing power to train their AI models. Many smaller players have been giving up on pretraining their models or otherwise shifting their strategy since the rise of DeepSeek, which broke the internet with R1, its open-source reasoning model that matches the performance of ChatGPT o1 but was built at a fraction of its cost. 

“DeepSeek is a moment of reckoning for the Chinese AI industry. The burning question shifted from ‘Who can make the best large language model?’ to ‘Who can use them better?’” says Hangcheng Cao, an assistant professor of information systems at Emory University. 

The rise of reasoning models like DeepSeek’s R1 and OpenAI’s ChatGPT o1 and o3 has also changed what businesses want from a data center. With this technology, most of the computing needs come from conducting step-by-step logical deductions in response to users’ queries, not from the process of training and creating the model in the first place. This reasoning process often yields better results but takes significantly more time. As a result, hardware with low latency (the time it takes for data to pass from one point on a network to another) is paramount. Data centers need to be located near major tech hubs to minimize transmission delays and ensure access to highly skilled operations and maintenance staff. 

This change means many data centers built in central, western, and rural China—where electricity and land are cheaper—are losing their allure to AI companies. In Zhengzhou, a city in Li’s home province of Henan, a newly built data center is even distributing free computing vouchers to local tech firms but still struggles to attract clients. 

Additionally, a lot of the new data centers that have sprung up in recent years were optimized for pretraining workloads—large, sustained computations run on massive data sets—rather than for inference, the process of running trained reasoning models to respond to user inputs in real time. Inference-friendly hardware differs from what’s traditionally used for large-scale AI training. 

GPUs like Nvidia H100 and A100 are designed for massive data processing, prioritizing speed and memory capacity. But as AI moves toward real-time reasoning, the industry seeks chips that are more efficient, responsive, and cost-effective. Even a minor miscalculation in infrastructure needs can render a data center suboptimal for the tasks clients require.

In these circumstances, the GPU rental price has dropped to an all-time low. A recent report from the Chinese media outlet Zhineng Yongxian said that an Nvidia H100 server configured with eight GPUs now rents for 75,000 yuan per month, down from highs of around 180,000. Some data centers would rather leave their facilities sitting empty than run the risk of losing even more money because they are so costly to run, says Fan: “The revenue from having a tiny part of the data center running simply wouldn’t cover the electricity and maintenance cost.”

“It’s paradoxical—China faces the highest acquisition costs for Nvidia chips, yet GPU leasing prices are extraordinarily low,” Li says. There’s an oversupply of computational power, especially in central and west China, but at the same time, there’s a shortage of cutting-edge chips. 

However, not all brokers were looking to make money from data centers in the first place. Instead, many were interested in gaming government benefits all along. Some operators exploit the sector for subsidized green electricity, obtaining permits to generate and sell power, according to Fang and some Chinese media reports. Instead of using the energy for AI workloads, they resell it back to the grid at a premium. In other cases, companies acquire land for data center development to qualify for state-backed loans and credits, leaving facilities unused while still benefiting from state funding, according to the local media outlet Jiazi Guangnian.

“Towards the end of 2024, no clear-headed contractor and broker in the market would still go into the business expecting direct profitability,” says Fang. “Everyone I met is leveraging the data center deal for something else the government could offer.”

A necessary evil

Despite the underutilization of data centers, China’s central government is still throwing its weight behind a push for AI infrastructure. In early 2025, it convened an AI industry symposium, emphasizing the importance of self-reliance in this technology. 

Major Chinese tech companies are taking note, making investments aligning with this national priority. Alibaba Group announced plans to invest over $50 billion in cloud computing and AI hardware infrastructure over the next three years, while ByteDance plans to invest around $20 billion in GPUs and data centers.

In the meantime, companies in the US are doing likewise. Major tech firms including OpenAI, Softbank, and Oracle have teamed up to commit to the Stargate initiative, which plans to invest up to $500 billion over the next four years to build advanced data centers and computing infrastructure. ​Given the AI competition between the two countries, experts say that China is unlikely to scale back its efforts. “If generative AI is going to be the killer technology, infrastructure is going to be the determinant of success,”  says Goodrich, the tech policy advisor to RAND.

“The Chinese central government will likely see [underused data centers] as a necessary evil to develop an important capability, a growing pain of sorts. You have the failed projects and distressed assets, and the state will consolidate and clean it up. They see the end, not the means,” Goodrich says.

Demand remains strong for Nvidia chips, and especially the H20 chip, which was custom-designed for the Chinese market. One industry source, who requested not to be identified under his company policy, confirmed that the H20, a lighter, faster model optimized for AI inference, is currently the most popular Nvidia chip, followed by the H100, which continues to flow steadily into China even though sales are officially restricted by US sanctions. Some of the new demand is driven by companies deploying their own versions of DeepSeek’s open-source models.

For now, many data centers in China sit in limbo—built for a future that has yet to arrive. Whether they will find a second life remains uncertain. For Fang Cunbao, DeepSeek’s success has become a moment of reckoning, casting doubt on the assumption that an endless expansion of AI infrastructure guarantees progress. That’s just a myth, he now realizes. At the start of this year, Fang decided to quit the data center industry altogether. “The market is too chaotic. The early adopters profited, but now it’s just people chasing policy loopholes,” he says. He’s decided to go into AI education next. 

“What stands between now and a future where AI is actually everywhere,” he says, “is not infrastructure anymore, but solid plans to deploy the technology.” 

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Schneider Electric to invest $700M in US manufacturing

Dive Brief: Automation manufacturer Schneider Electric plans to invest $700 million in its U.S. operations through 2027, the company announced Tuesday. The money will go toward facility upgrades, expansions and openings across eight sites in Texas, Tennessee, Ohio, North Carolina, Massachusetts and Missouri. Schneider expects to create more than 1,000 jobs.  The move marks Schneider’s largest-ever investment in the U.S., as the company aims to meet rising demand across its data center, utilities, manufacturing and energy infrastructure segments. Dive Insight: Schneider’s announcement is part of a larger $1 billion investment the company is making in the U.S. this decade.  Artificial intelligence-driven demand for data centers and electrical infrastructure is driving the need for heightened spending on electrical grid-related needs. Data center electricity demand could double by 2030 — consuming up to 9% of the country’s electricity generation, according to a May 2024 study by the Electric Power Research Institute. “We stand at an inflection point for the technology and industrial sectors in the U.S., driven by incredible AI growth and unprecedented energy demand,” Aamir Paul, president of North America Operations for Schneider Electric, said in a statement.  Schneider has been pushing a localization strategy in recent months, with a goal to locally source and produce roughly 90% of sales in each region. That push could help the company weather the Trump administration’s tariffs on Mexico, where Schneider has much of its North American production.  CFO Hilary Maxson said on a recent earnings call that the company is watching for any reciprocal tariffs that may impact their operations. If the United States-Mexico-Canada Agreement remains in place, Maxson said the impact to Schneider would likely be “immaterial.” If the trade deal and free trade zones are repealed, however, the CFO added that the hit to the company could be greater.  “We’re really

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Utilities should develop data center tariffs to protect consumers, decarbonize: SWEEP

With data center electricity demand on the rise across the U.S., utilities should develop specialized tariffs to protect consumers and keep their grids green when these large load customers interconnect, the Southwest Energy Efficiency Project said Thursday in a report. “While AI offers the potential for significant economic and social benefits, there are growing concerns with the rapid increases in electricity demand from data centers and how they will impact the power sector and state and utility climate goals,” SWEEP said. Data centers today account for about 4.5% of U.S. electricity consumption, according to the analysis. But in its most recent report to Congress, the Lawrence Berkeley National Lab projected data centers could account for up to 12% of U.S. electricity use by 2028, SWEEP said. EPRI recently surveyed 25 utilities nationally and found almost half have received requests for new data center facilities with loads larger than 1,000 MW. And “almost half of the utilities surveyed have received data center requests that exceed 50% of their current system peak demand,” SWEEP said. The potential load additions “pose two types of threats to state greenhouse [gas] emission reduction goals,” SWEEP said: Utilities could add or utilize more fossil-fuel based generation, and they could struggle to add sufficient renewables to meet demand from the electrification of vehicles, buildings and industry, the report warned. To address these risks, SWEEP recommends that utilities ensure new data center customers — and other new industrial or commercial customers with demands over 50 MW, or combined demands from several facilities of more than 100 MW — “commit to providing sufficient revenue, over a contract period such as 12 years, to cover the generation and transmission investments made on their behalf.” Utilities should also “propose and attempt to get approval for tariffs that require new large data center customers, and other new

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Airtel connects India with 100Tbps submarine cable

“Businesses are becoming increasingly global and digital-first, with industries such as financial services, data centers, and social media platforms relying heavily on real-time, uninterrupted data flow,” Sinha added. The 2Africa Pearls submarine cable system spans 45,000 kilometers, involving a consortium of global telecommunications leaders including Bayobab, China Mobile International, Meta, Orange, Telecom Egypt, Vodafone Group, and WIOCC. Alcatel Submarine Networks is responsible for the cable’s manufacturing and installation, the statement added. This cable system is part of a broader global effort to enhance international digital connectivity. Unlike traditional telecommunications infrastructure, the 2Africa Pearls project represents a collaborative approach to solving complex global communication challenges. “The 100 Tbps capacity of the 2Africa Pearls cable significantly surpasses most existing submarine cable systems, positioning India as a key hub for high-speed connectivity between Africa, Europe, and Asia,” said Prabhu Ram, VP for Industry Research Group at CyberMedia Research. According to Sinha, Airtel’s infrastructure now spans “over 400,000 route kilometers across 34+ cables, connecting 50 countries across five continents. This expansive infrastructure ensures businesses and individuals stay seamlessly connected, wherever they are.” Gogia further emphasizes the broader implications, noting, “What also stands out is the partnership behind this — Airtel working with Meta and center3 signals a broader shift. India is no longer just a consumer of global connectivity. We’re finally shaping the routes, not just using them.”

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Former Arista COO launches NextHop AI for customized networking infrastructure

Sadana argued that unlike traditional networking where an IT person can just plug a cable into a port and it works, AI networking requires intricate, custom solutions. The core challenge is creating highly optimized, efficient networking infrastructure that can support massive AI compute clusters with minimal inefficiencies. How NextHop is looking to change the game for hyperscale networking NextHop AI is working directly alongside its hyperscaler customers to develop and build customized networking solutions. “We are here to build the most efficient AI networking solutions that are out there,” Sadana said. More specifically, Sadana said that NextHop is looking to help hyperscalers in several ways including: Compressing product development cycles: “Companies that are doing things on their own can compress their product development cycle by six to 12 months when they partner with us,” he said. Exploring multiple technological alternatives: Sadana noted that hyperscalers might try and build on their own and will often only be able to explore one or two alternative approaches. With NextHop, Sadana said his company will enable them to explore four to six different alternatives. Achieving incremental efficiency gains: At the massive cloud scale that hyperscalers operate, even an incremental one percent improvement can have an oversized outcome. “You have to make AI clusters as efficient as possible for the world to use all the AI applications at the right cost structure, at the right economics, for this to be successful,” Sadana said. “So we are participating by making that infrastructure layer a lot more efficient for cloud customers, or the hyperscalers, which, in turn, of course, gives the benefits to all of these software companies trying to run AI applications in these cloud companies.” Technical innovations: Beyond traditional networking In terms of what the company is actually building now, NextHop is developing specialized network switches

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Microsoft abandons data center projects as OpenAI considers its own, hinting at a market shift

A potential ‘oversupply position’ In a new research note, TD Cowan analysts reportedly said that Microsoft has walked away from new data center projects in the US and Europe, purportedly due to an oversupply of compute clusters that power AI. This follows reports from TD Cowen in February that Microsoft had “cancelled leases in the US totaling a couple of hundred megawatts” of data center capacity. The researchers noted that the company’s pullback was a sign of it “potentially being in an oversupply position,” with demand forecasts lowered. OpenAI, for its part, has reportedly discussed purchasing billions of dollars’ worth of data storage hardware and software to increase its computing power and decrease its reliance on hyperscalers. This fits with its planned Stargate Project, a $500 billion, US President Donald Trump-endorsed initiative to build out its AI infrastructure in the US over the next four years. Based on the easing of exclusivity between the two companies, analysts say these moves aren’t surprising. “When looking at storage in the cloud — especially as it relates to use in AI — it is incredibly expensive,” said Matt Kimball, VP and principal analyst for data center compute and storage at Moor Insights & Strategy. “Those expenses climb even higher as the volume of storage and movement of data grows,” he pointed out. “It is only smart for any business to perform a cost analysis of whether storage is better managed in the cloud or on-prem, and moving forward in a direction that delivers the best performance, best security, and best operational efficiency at the lowest cost.”

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PEAK:AIO adds power, density to AI storage server

There is also the fact that many people working with AI are not IT professionals, such as professors, biochemists, scientists, doctors, clinicians, and they don’t have a traditional enterprise department or a data center. “It’s run by people that wouldn’t really know, nor want to know, what storage is,” he said. While the new AI Data Server is a Dell design, PEAK:AIO has worked with Lenovo, Supermicro, and HPE as well as Dell over the past four years, offering to convert their off the shelf storage servers into hyper fast, very AI-specific, cheap, specific storage servers that work with all the protocols at Nvidia, like NVLink, along with NFS and NVMe over Fabric. It also greatly increased storage capacity by going with 61TB drives from Solidigm. SSDs from the major server vendors typically maxed out at 15TB, according to the vendor. PEAK:AIO competes with VAST, WekaIO, NetApp, Pure Storage and many others in the growing AI workload storage arena. PEAK:AIO’s AI Data Server is available now.

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SoftBank to buy Ampere for $6.5B, fueling Arm-based server market competition

SoftBank’s announcement suggests Ampere will collaborate with other SBG companies, potentially creating a powerful ecosystem of Arm-based computing solutions. This collaboration could extend to SoftBank’s numerous portfolio companies, including Korean/Japanese web giant LY Corp, ByteDance (TikTok’s parent company), and various AI startups. If SoftBank successfully steers its portfolio companies toward Ampere processors, it could accelerate the shift away from x86 architecture in data centers worldwide. Questions remain about Arm’s server strategy The acquisition, however, raises questions about how SoftBank will balance its investments in both Arm and Ampere, given their potentially competing server CPU strategies. Arm’s recent move to design and sell its own server processors to Meta signaled a major strategic shift that already put it in direct competition with its own customers, including Qualcomm and Nvidia. “In technology licensing where an entity is both provider and competitor, boundaries are typically well-defined without special preferences beyond potential first-mover advantages,” Kawoosa explained. “Arm will likely continue making independent licensing decisions that serve its broader interests rather than favoring Ampere, as the company can’t risk alienating its established high-volume customers.” Industry analysts speculate that SoftBank might position Arm to focus on custom designs for hyperscale customers while allowing Ampere to dominate the market for more standardized server processors. Alternatively, the two companies could be merged or realigned to present a unified strategy against incumbents Intel and AMD. “While Arm currently dominates processor architecture, particularly for energy-efficient designs, the landscape isn’t static,” Kawoosa added. “The semiconductor industry is approaching a potential inflection point, and we may witness fundamental disruptions in the next 3-5 years — similar to how OpenAI transformed the AI landscape. SoftBank appears to be maximizing its Arm investments while preparing for this coming paradigm shift in processor architecture.”

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Nvidia, xAI and two energy giants join genAI infrastructure initiative

The new AIP members will “further strengthen the partnership’s technology leadership as the platform seeks to invest in new and expanded AI infrastructure. Nvidia will also continue in its role as a technical advisor to AIP, leveraging its expertise in accelerated computing and AI factories to inform the deployment of next-generation AI data center infrastructure,” the group’s statement said. “Additionally, GE Vernova and NextEra Energy have agreed to collaborate with AIP to accelerate the scaling of critical and diverse energy solutions for AI data centers. GE Vernova will also work with AIP and its partners on supply chain planning and in delivering innovative and high efficiency energy solutions.” The group claimed, without offering any specifics, that it “has attracted significant capital and partner interest since its inception in September 2024, highlighting the growing demand for AI-ready data centers and power solutions.” The statement said the group will try to raise “$30 billion in capital from investors, asset owners, and corporations, which in turn will mobilize up to $100 billion in total investment potential when including debt financing.” Forrester’s Nguyen also noted that the influence of two of the new members — xAI, owned by Elon Musk, along with Nvidia — could easily help with fundraising. Musk “with his connections, he does not make small quiet moves,” Nguyen said. “As for Nvidia, they are the face of AI. Everything they do attracts attention.” Info-Tech’s Bickley said that the astronomical dollars involved in genAI investments is mind-boggling. And yet even more investment is needed — a lot more.

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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.

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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

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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

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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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