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🚨🇨🇳China's Underwater Data Centers: Huge Win in the AI Power Race Deep under the South China Sea, China just took a major step toward AI supremacy. 🔸From Navy Tech to Commercial Compute China has launched its first commercial underwater data center off Hainan Island. Sitting 35 meters below the...

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NewRulesGeopolitics

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10,686 просмотров • 4 месяцев назад •via X (Twitter)

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🚨🇨🇳 China unveils its first full-stack green AI computing platform The platform was launched in the city of Hohhot in north China's Inner Mongolia Autonomous Region, CCTV reports 🔸The goal is to unify computing resources, cut costs, and accelerate nationwide AI adoption through low-carbon infrastructure 🔸The platform is fully compatible with domestically produced chips and mainstream computing architectures 🔸Connects over 10 leading models from major telecom operators and tech firms This signals a major push toward scalable, energy-efficient national AI infrastructure built to support the next wave of model deployment and AI-driven services
1:11

🚨🇨🇳 China unveils its first full-stack green AI computing platform The platform was launched in the city of Hohhot in north China's Inner Mongolia Autonomous Region, CCTV reports 🔸The goal is to unify computing resources, cut costs, and accelerate nationwide AI adoption through low-carbon infrastructure 🔸The platform is fully compatible with domestically produced chips and mainstream computing architectures 🔸Connects over 10 leading models from major telecom operators and tech firms This signals a major push toward scalable, energy-efficient national AI infrastructure built to support the next wave of model deployment and AI-driven services

Sputnik

19,070 просмотров • 2 месяцев назад

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”
4:51

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

DogeDesigner

244,339 просмотров • 23 дней назад

🇨🇳 CHINA IS BUILDING AN AI ARMY IN THE DESERT WITH BANNED U.S. TECH China is planting 40 massive data centers in the Xinjiang desert, each packed with banned Nvidia chips. The aim isn’t subtle. They’re building a domestic AI force to match or surpass anything in the West. This isn’t theory, it’s hardware in motion, with government backing and national ambition baked in. Power, chips, and infrastructure are all being scaled at breakneck speed. While the U.S. cut off the supply, China’s response is to clone the machine and grow it bigger. The desert is now a launchpad. Source: *Walter Bloomberg
1:29

🇨🇳 CHINA IS BUILDING AN AI ARMY IN THE DESERT WITH BANNED U.S. TECH China is planting 40 massive data centers in the Xinjiang desert, each packed with banned Nvidia chips. The aim isn’t subtle. They’re building a domestic AI force to match or surpass anything in the West. This isn’t theory, it’s hardware in motion, with government backing and national ambition baked in. Power, chips, and infrastructure are all being scaled at breakneck speed. While the U.S. cut off the supply, China’s response is to clone the machine and grow it bigger. The desert is now a launchpad. Source: *Walter Bloomberg

Mario Nawfal

108,854 просмотров • 1 год назад

The AI infrastructure race just went underwater. A Portland, Oregon startup called Panthalassa just raised $140 million in a Series B round led by Peter Thiel, the idea sounds wild until you understand the physics. Every AI data center on Earth has the same three problems, it needs massive amounts of electricity, it generates enormous heat that has to be cooled, and it requires land in places that are already running out of grid capacity. Panthalassa's answer is to eliminate all three constraints at once by taking the data center off the grid, off the land, and into the open ocean. And here's how it works, the company builds autonomous, self-propelled floating nodes made from plate steel, no anchor, no fuel, no cable to shore. As waves lift the platform, water is forced through an internal turbine, generating electricity continuously. That electricity runs AI inference chips onboard and the results go back to shore via low-Earth-orbit satellite. The surrounding ocean provides free supercooling, which one investor estimates could generate power at roughly two cents per kilowatt-hour. For context on why this matters, land based data centers spend up to 40% of their total energy budget just on cooling. Microsoft's Project Natick found that submerged servers had a failure rate of just 0.7% compared to 5.9% on land. The ocean doesn't just solve the cost problem but it solves the reliability problem too. Panthalassa's Ocean 3 pilot nodes are already under construction, with deployment in the northern Pacific targeted for August 2026 and commercial operations in 2027. The company has been building toward this for a decade with Ocean-1, Ocean-2, and Wavehopper prototypes already validated at sea, including a test in Puget Sound in 2024. The global underwater data center market was $3.2 billion in 2025 and is projected to reach $14.8 billion by 2034. China has already launched commercial-scale undersea data centers. The race to compute off the grid whether in space, underwater, or on the open ocean is no longer theoretical, it's being funded, permitted, and in Panthalassa's case, it's being built right now. The future is bright!
1:53

The AI infrastructure race just went underwater. A Portland, Oregon startup called Panthalassa just raised $140 million in a Series B round led by Peter Thiel, the idea sounds wild until you understand the physics. Every AI data center on Earth has the same three problems, it needs massive amounts of electricity, it generates enormous heat that has to be cooled, and it requires land in places that are already running out of grid capacity. Panthalassa's answer is to eliminate all three constraints at once by taking the data center off the grid, off the land, and into the open ocean. And here's how it works, the company builds autonomous, self-propelled floating nodes made from plate steel, no anchor, no fuel, no cable to shore. As waves lift the platform, water is forced through an internal turbine, generating electricity continuously. That electricity runs AI inference chips onboard and the results go back to shore via low-Earth-orbit satellite. The surrounding ocean provides free supercooling, which one investor estimates could generate power at roughly two cents per kilowatt-hour. For context on why this matters, land based data centers spend up to 40% of their total energy budget just on cooling. Microsoft's Project Natick found that submerged servers had a failure rate of just 0.7% compared to 5.9% on land. The ocean doesn't just solve the cost problem but it solves the reliability problem too. Panthalassa's Ocean 3 pilot nodes are already under construction, with deployment in the northern Pacific targeted for August 2026 and commercial operations in 2027. The company has been building toward this for a decade with Ocean-1, Ocean-2, and Wavehopper prototypes already validated at sea, including a test in Puget Sound in 2024. The global underwater data center market was $3.2 billion in 2025 and is projected to reach $14.8 billion by 2034. China has already launched commercial-scale undersea data centers. The race to compute off the grid whether in space, underwater, or on the open ocean is no longer theoretical, it's being funded, permitted, and in Panthalassa's case, it's being built right now. The future is bright!

Milk Road AI

133,232 просмотров • 3 месяцев назад

🚨 Hasan Piker’s Producer Cheers China’s AI Rise and Patriotic Robots While Fearing America’s Tech Will Be Used Against Him This is Eric Hovagim, a producer and researcher for Hasan Piker, reporting from China that America is “getting lapped” technologically. Hovagim praises China’s state-directed approach to technological development and argues that people there view AI as a tool of “liberation.” He then excitedly describes Beijing building an international AI coalition spanning much of the Global South, including BRICS, the African Union, the Arab League, CARICOM, and the Shanghai Cooperation Organization. This clip is a good example of why Americans should bring more nuance to debates over AI and data-center development. Legitimate concerns deserve serious debate, but reflexive opposition also carries costs, especially while China is treating AI infrastructure as a source of national strength and global influence. In China, AI and the infrastructure needed to power it are presented as engines of national progress, liberation, and Global South power. Here in the United States, AI is increasingly portrayed as inherently sinister, its companies are targeted by far-left activists, and opposition to data-center development has become one of the left’s most popular organizing causes. The contrast should be obvious. China is building with the intention of leading the future, while much of the American left is organizing against the infrastructure the United States will need to compete.
2:21

🚨 Hasan Piker’s Producer Cheers China’s AI Rise and Patriotic Robots While Fearing America’s Tech Will Be Used Against Him This is Eric Hovagim, a producer and researcher for Hasan Piker, reporting from China that America is “getting lapped” technologically. Hovagim praises China’s state-directed approach to technological development and argues that people there view AI as a tool of “liberation.” He then excitedly describes Beijing building an international AI coalition spanning much of the Global South, including BRICS, the African Union, the Arab League, CARICOM, and the Shanghai Cooperation Organization. This clip is a good example of why Americans should bring more nuance to debates over AI and data-center development. Legitimate concerns deserve serious debate, but reflexive opposition also carries costs, especially while China is treating AI infrastructure as a source of national strength and global influence. In China, AI and the infrastructure needed to power it are presented as engines of national progress, liberation, and Global South power. Here in the United States, AI is increasingly portrayed as inherently sinister, its companies are targeted by far-left activists, and opposition to data-center development has become one of the left’s most popular organizing causes. The contrast should be obvious. China is building with the intention of leading the future, while much of the American left is organizing against the infrastructure the United States will need to compete.

Stu Smith

87,378 просмотров • 16 дней назад

.Kevin O'Leary aka Mr. Wonderful is right that China is trying to convince America to stop building AI data centers while building tons of its own. That's China's plan to win the AI race and control the next century. But that DOES NOT mean data centers should just drain public power. I'm dead set against any data center that has a SINGLE line feeding them power from the grid. But some are generating their own power and giving the excess TO the grid. Those are two very different scenarios. Thankfully, Kevin O'Leary has said his data center project in Utah will generate its own power. THIS IS A MUST. Just like railroads, factories, oil, and electricity powered previous generations, the future will run on AI computing, powered by massive amounts of energy. We need both to stop China from dominating the world. China knows this and is trying to turn data centers into a boogeyman. There’s evidence out now from the Bitcoin Policy Institute that foreign influence networks are actively feeding this anti-data center movement in the United States. They traced several overlapping campaigns, all aimed at slowing American AI infrastructure down, directly to China. Strong countries build infrastructure. Weak countries fear it. We can't fall for their trap. But we must also build strategically without sacrificing public resources.
11:49

.Kevin O'Leary aka Mr. Wonderful is right that China is trying to convince America to stop building AI data centers while building tons of its own. That's China's plan to win the AI race and control the next century. But that DOES NOT mean data centers should just drain public power. I'm dead set against any data center that has a SINGLE line feeding them power from the grid. But some are generating their own power and giving the excess TO the grid. Those are two very different scenarios. Thankfully, Kevin O'Leary has said his data center project in Utah will generate its own power. THIS IS A MUST. Just like railroads, factories, oil, and electricity powered previous generations, the future will run on AI computing, powered by massive amounts of energy. We need both to stop China from dominating the world. China knows this and is trying to turn data centers into a boogeyman. There’s evidence out now from the Bitcoin Policy Institute that foreign influence networks are actively feeding this anti-data center movement in the United States. They traced several overlapping campaigns, all aimed at slowing American AI infrastructure down, directly to China. Strong countries build infrastructure. Weak countries fear it. We can't fall for their trap. But we must also build strategically without sacrificing public resources.

Glenn Beck

188,156 просмотров • 2 месяцев назад

🚨🇷🇺Russia Turns Siberia Into an AI Powerhouse While the US and Europe face high electricity prices that slow down AI growth, Russia is making a smart move: building huge data centers in cold Siberia and the Far East. These regions used to export just oil and gas — now they're starting to export AI computing power. 🔸 Russia has 194 commercial data centers. Moscow once had 85% of them, but Siberia and the Far East now hold over 15% — and that share is growing fast. 🔸 Siberia’s cold air cools servers for 8–9 months a year, making power efficiency excellent. Hot, humid China has much lower efficiency. 🔸 In Russian Far East special zones, electricity costs just $0.045–0.065 per kWh — 2 to 2.5 times cheaper than in eastern China. Running a 10 MW server farm costs about $475,000 a month in Russia, compared to over $1.1 million in Shanghai. 🔸 Russia freed up 1.5–2 GW of power by cracking down on illegal crypto mining, which was using 2.5–3 GW, mostly in Siberia. There’s also extra clean hydro power from large dams. 🔸 Big Chinese companies like Alibaba, Tencent, and carmakers Haval, Chery, and Geely are moving in. Chinese EV companies have raised spending on Russian cloud services 13 times. They get cheaper, greener power and must follow Russian data laws — all while staying very close to China for fast connections. Could Siberia’s cheap AI power leave the West behind in the neuro-age?
0:20

🚨🇷🇺Russia Turns Siberia Into an AI Powerhouse While the US and Europe face high electricity prices that slow down AI growth, Russia is making a smart move: building huge data centers in cold Siberia and the Far East. These regions used to export just oil and gas — now they're starting to export AI computing power. 🔸 Russia has 194 commercial data centers. Moscow once had 85% of them, but Siberia and the Far East now hold over 15% — and that share is growing fast. 🔸 Siberia’s cold air cools servers for 8–9 months a year, making power efficiency excellent. Hot, humid China has much lower efficiency. 🔸 In Russian Far East special zones, electricity costs just $0.045–0.065 per kWh — 2 to 2.5 times cheaper than in eastern China. Running a 10 MW server farm costs about $475,000 a month in Russia, compared to over $1.1 million in Shanghai. 🔸 Russia freed up 1.5–2 GW of power by cracking down on illegal crypto mining, which was using 2.5–3 GW, mostly in Siberia. There’s also extra clean hydro power from large dams. 🔸 Big Chinese companies like Alibaba, Tencent, and carmakers Haval, Chery, and Geely are moving in. Chinese EV companies have raised spending on Russian cloud services 13 times. They get cheaper, greener power and must follow Russian data laws — all while staying very close to China for fast connections. Could Siberia’s cheap AI power leave the West behind in the neuro-age?

NewRulesGeopolitics

36,918 просмотров • 3 месяцев назад

🚨🇨🇳🇺🇸PENTAGON IN PANIC MODE: CHINA WILL MATCH U.S. NAVAL POWER BY THE 2030s America is showing off its huge navy in the Iran war — with 20 warships, 3 aircraft carriers, and over 100 daily strikes from far away. But China is watching — and saying: "That's exactly how NOT to do it." Beijing is following the same global strength, but smarter. 🔸 The US Navy is the only fleet today that can sustain months-long, high-intensity operations thousands of km from home bases — China is rapidly closing that gap. 🔸 China is expanding its fleet with new aircraft carriers, helicopter carriers, and large landing ships specifically designed for operations far beyond Taiwan. 🔸 By the early 2030s, China will be ready for complex missions like supporting friendly countries with sea and air forces — according to expert Sidharth Kaushal of the Royal United Services Institute (RUSI.) 🔸 Key challenges remain: the “first island chain” (Japan-Taiwan-Philippines) blocks easy access to the open ocean, only one overseas base in Djibouti, and a remaining lag in quiet nuclear-powered attack submarines. 🔸 China is investing in big manned warships as essential command centers for drones, lasers, railguns, and energy weapons — plus they handle real-world diplomatic tasks like boarding ships to control sea routes 🔸 Instead of copying the US model, China is carefully analyzing its weaknesses: vulnerable carriers, heavy reliance on complex supply lines, and the steep political price of endless global missions. 🔸 This is shaping a smarter alternative strategy — prioritizing strong regional dominance supported by advanced tech, drone swarms, and asymmetric tools rather than pure worldwide power projection. Do you think China will have a powerful global naval presence by 2030?
1:03

🚨🇨🇳🇺🇸PENTAGON IN PANIC MODE: CHINA WILL MATCH U.S. NAVAL POWER BY THE 2030s America is showing off its huge navy in the Iran war — with 20 warships, 3 aircraft carriers, and over 100 daily strikes from far away. But China is watching — and saying: "That's exactly how NOT to do it." Beijing is following the same global strength, but smarter. 🔸 The US Navy is the only fleet today that can sustain months-long, high-intensity operations thousands of km from home bases — China is rapidly closing that gap. 🔸 China is expanding its fleet with new aircraft carriers, helicopter carriers, and large landing ships specifically designed for operations far beyond Taiwan. 🔸 By the early 2030s, China will be ready for complex missions like supporting friendly countries with sea and air forces — according to expert Sidharth Kaushal of the Royal United Services Institute (RUSI.) 🔸 Key challenges remain: the “first island chain” (Japan-Taiwan-Philippines) blocks easy access to the open ocean, only one overseas base in Djibouti, and a remaining lag in quiet nuclear-powered attack submarines. 🔸 China is investing in big manned warships as essential command centers for drones, lasers, railguns, and energy weapons — plus they handle real-world diplomatic tasks like boarding ships to control sea routes 🔸 Instead of copying the US model, China is carefully analyzing its weaknesses: vulnerable carriers, heavy reliance on complex supply lines, and the steep political price of endless global missions. 🔸 This is shaping a smarter alternative strategy — prioritizing strong regional dominance supported by advanced tech, drone swarms, and asymmetric tools rather than pure worldwide power projection. Do you think China will have a powerful global naval presence by 2030?

NewRulesGeopolitics

17,795 просмотров • 3 месяцев назад

🇨🇳📱 China Isn’t Talking About the AI Future: It’s Already Living It While Western media is still debating what AI wearables might look like, China is already living in that future, in shops, offices, classrooms, metros and even family homes. A new report shows just how far ahead China actually is: - 70+ Chinese companies have launched smart-glasses since Meta’s 2023 release. - Devices from Inmo, Rokid, Xiaomi, Alibaba are everywhere, some selling overseas, others powering China’s domestic AI ecosystems. - Features like paying for items by glancing at a QR code already exist in China. - China’s AI hardware sector is booming while others are still imagining prototypes. Kai-Fu Lee sums it up perfectly: “China is the nation of manufacturing… the next phase of competition will move to devices.” Exactly. The U.S. builds models. China builds the whole world those models have to run in. Why China Is Leading the AI Device Race? 1️⃣ The world’s strongest hardware ecosystem No country on Earth can design, prototype, manufacture and scale AI devices as fast as China. The ability to build millions of units in months gives China a massive advantage the U.S. simply doesn’t have. 2️⃣ Seamless digital integration Smart glasses plug directly into China’s existing infrastructure: - QR payments - WeChat / Alipay ecosystems - AI-enhanced navigation - Real-time translation and AR overlays The West has apps. China has an entire society built for AI adoption. 3️⃣ Relentless experimentation China is testing everything from: - enterprise AI recorders (DingTalk A1) - Plaud-style pocket transcribers - education-focused AI translators - even the quirky “Native Language Star” muzzle device for English practice Other countries theorise about “the next big thing.” China builds 20 versions of it by lunchtime. 4️⃣ The feedback loop advantage More devices, more usage, more data, faster improvement. China’s scale accelerates AI development in ways the U.S. cannot match. 5️⃣ AI wearables are already normal here Smart eyewear, AI notetakers, live translators, all in daily use. This isn’t sci-fi. This is Tuesday in Beijing. The Global Reality: For China, the AI device race is not hypothetical, it’s happening at street level. For many outside China? They can only imagine this kind of tech and often don’t believe it when they hear about it. Anyone living in China already sees the future everywhere, in convenience stores, on commuters, in classrooms and in the hands of office workers. Meanwhile, most of the world is still arguing about “what AI devices might eventually look like.” The Hard Truth: If the next “iPhone moment” of the AI age is going to come from anywhere, the smart money is on China, a country where hardware innovation, manufacturing power and real-world adoption all move at a speed the West simply can’t match.
1:05

🇨🇳📱 China Isn’t Talking About the AI Future: It’s Already Living It While Western media is still debating what AI wearables might look like, China is already living in that future, in shops, offices, classrooms, metros and even family homes. A new report shows just how far ahead China actually is: - 70+ Chinese companies have launched smart-glasses since Meta’s 2023 release. - Devices from Inmo, Rokid, Xiaomi, Alibaba are everywhere, some selling overseas, others powering China’s domestic AI ecosystems. - Features like paying for items by glancing at a QR code already exist in China. - China’s AI hardware sector is booming while others are still imagining prototypes. Kai-Fu Lee sums it up perfectly: “China is the nation of manufacturing… the next phase of competition will move to devices.” Exactly. The U.S. builds models. China builds the whole world those models have to run in. Why China Is Leading the AI Device Race? 1️⃣ The world’s strongest hardware ecosystem No country on Earth can design, prototype, manufacture and scale AI devices as fast as China. The ability to build millions of units in months gives China a massive advantage the U.S. simply doesn’t have. 2️⃣ Seamless digital integration Smart glasses plug directly into China’s existing infrastructure: - QR payments - WeChat / Alipay ecosystems - AI-enhanced navigation - Real-time translation and AR overlays The West has apps. China has an entire society built for AI adoption. 3️⃣ Relentless experimentation China is testing everything from: - enterprise AI recorders (DingTalk A1) - Plaud-style pocket transcribers - education-focused AI translators - even the quirky “Native Language Star” muzzle device for English practice Other countries theorise about “the next big thing.” China builds 20 versions of it by lunchtime. 4️⃣ The feedback loop advantage More devices, more usage, more data, faster improvement. China’s scale accelerates AI development in ways the U.S. cannot match. 5️⃣ AI wearables are already normal here Smart eyewear, AI notetakers, live translators, all in daily use. This isn’t sci-fi. This is Tuesday in Beijing. The Global Reality: For China, the AI device race is not hypothetical, it’s happening at street level. For many outside China? They can only imagine this kind of tech and often don’t believe it when they hear about it. Anyone living in China already sees the future everywhere, in convenience stores, on commuters, in classrooms and in the hands of office workers. Meanwhile, most of the world is still arguing about “what AI devices might eventually look like.” The Hard Truth: If the next “iPhone moment” of the AI age is going to come from anywhere, the smart money is on China, a country where hardware innovation, manufacturing power and real-world adoption all move at a speed the West simply can’t match.

James Wood 武杰士

17,430 просмотров • 8 месяцев назад

🚨🇺🇸🇨🇳 U.S. UNDERSEA NIGHTMARE: CHINA RACING FOR SUBMARINES + SEABED GOLD Trump heads to Beijing next week as US experts panic. China just hit a major breakthrough in deep-sea mining tech — robots, pumps and smart systems that pull minerals from 4,000+ meter depths — while building advanced nuclear submarines. This combo threatens America’s naval power and control over battery metals for EVs and renewables. 🔸 China closing the gap fast on US undersea dominance with modern subs and dual-use mining robots that also boost underwater spying. 🔸 Massive seabed riches: Clarion-Clipperton Zone alone holds 21 billion tonnes of nodules — including 274 million tonnes nickel (6x land reserves) and 29 million tonnes cobalt (3x land reserves.) 🔸 Trump bypassed UN rules via executive order to rush US seabed mining after China restricted rare earths. 🔸 Battery metal boom: Nickel demand for EVs jumps from 200,000 tonnes (2023) to 1.3 million tonnes by 2030 — China’s refining dominance + new mining tech gives it huge leverage. 🔸 China holds 5 International Seabed Authority exploration contracts, operates at 4,000m+ depths, and developed smart pump-pipeline systems for harsh ocean conditions since the 1980s. Do you think the U.S. has lost its technological edge in subsea technology?
0:38

🚨🇺🇸🇨🇳 U.S. UNDERSEA NIGHTMARE: CHINA RACING FOR SUBMARINES + SEABED GOLD Trump heads to Beijing next week as US experts panic. China just hit a major breakthrough in deep-sea mining tech — robots, pumps and smart systems that pull minerals from 4,000+ meter depths — while building advanced nuclear submarines. This combo threatens America’s naval power and control over battery metals for EVs and renewables. 🔸 China closing the gap fast on US undersea dominance with modern subs and dual-use mining robots that also boost underwater spying. 🔸 Massive seabed riches: Clarion-Clipperton Zone alone holds 21 billion tonnes of nodules — including 274 million tonnes nickel (6x land reserves) and 29 million tonnes cobalt (3x land reserves.) 🔸 Trump bypassed UN rules via executive order to rush US seabed mining after China restricted rare earths. 🔸 Battery metal boom: Nickel demand for EVs jumps from 200,000 tonnes (2023) to 1.3 million tonnes by 2030 — China’s refining dominance + new mining tech gives it huge leverage. 🔸 China holds 5 International Seabed Authority exploration contracts, operates at 4,000m+ depths, and developed smart pump-pipeline systems for harsh ocean conditions since the 1980s. Do you think the U.S. has lost its technological edge in subsea technology?

NewRulesGeopolitics

12,764 просмотров • 3 месяцев назад

China just put a massive data center... UNDER THE SEA!😳😳 Forget massive air conditioners and cooling towers. In Shanghai, a 2,000-ton "steel beast" is now running servers 10 meters deep in the ocean. Why? Because the sea is the ultimate natural cooler! With an average water temperature of just 15°C, it keeps high-speed servers running perfectly. The results are mind-blowing: Saves 40,000 tons of fresh water a year (enough for one family for 100 years!). Powered directly by a nearby offshore wind farm just 500 meters away. Green energy meets deep-sea computing. This isn't sci-fi anymore; it's happening right now in China!!🍀🇨🇳👍
0:41

China just put a massive data center... UNDER THE SEA!😳😳 Forget massive air conditioners and cooling towers. In Shanghai, a 2,000-ton "steel beast" is now running servers 10 meters deep in the ocean. Why? Because the sea is the ultimate natural cooler! With an average water temperature of just 15°C, it keeps high-speed servers running perfectly. The results are mind-blowing: Saves 40,000 tons of fresh water a year (enough for one family for 100 years!). Powered directly by a nearby offshore wind farm just 500 meters away. Green energy meets deep-sea computing. This isn't sci-fi anymore; it's happening right now in China!!🍀🇨🇳👍

AL-fira 🇨🇳

12,217 просмотров • 2 месяцев назад

.Kevin O'Leary aka Mr. Wonderful breaks down the brutal reality of the AI race with China and proposes a new rule for data centers moving forward in the U.S.: “The new rule of engagement should be this, as part of the national solution: if I'm going to bring a data center anywhere, I gotta bring my own power. I gotta spend billions to build power and then give some of it back to the grid so we can stay competitive. I've been dealing in China since they came into the World Trade Organization back in 2000. They haven't played by the rules, before we were even worried about AI… In the last 18 months, the Chinese, using primarily coal-burning turbines because they don't care about the environment, or [if] they need a permit - the big guy says, ‘put one here,’ and that's how it happens - they've built 400 gigawatts of new power for China, its economy, and its efforts to dominate AI. How many have we built that's new in America? Zero.”
3:04

.Kevin O'Leary aka Mr. Wonderful breaks down the brutal reality of the AI race with China and proposes a new rule for data centers moving forward in the U.S.: “The new rule of engagement should be this, as part of the national solution: if I'm going to bring a data center anywhere, I gotta bring my own power. I gotta spend billions to build power and then give some of it back to the grid so we can stay competitive. I've been dealing in China since they came into the World Trade Organization back in 2000. They haven't played by the rules, before we were even worried about AI… In the last 18 months, the Chinese, using primarily coal-burning turbines because they don't care about the environment, or [if] they need a permit - the big guy says, ‘put one here,’ and that's how it happens - they've built 400 gigawatts of new power for China, its economy, and its efforts to dominate AI. How many have we built that's new in America? Zero.”

Glenn Beck

37,859 просмотров • 1 месяц назад

🚨Russia's Winning Military AI Race🇷🇺 Despite Western sanctions, Russia is making tangible, battlefield-proven progress in military AI. 🔸Ukrainian soldiers report Shahed drones now perform evasive maneuvers, likely using rear cameras & onboard AI to dodge interceptors. 🔸Lancet munitions & elite drone units use neural networks for precision strikes Russia's Playbook: 🔸Battlefield Feedback Loop: Direct data from Ukraine refines AI systems. 🔸Civilian Mobilization: Volunteers crowdsource data to train models. 🔸Software over Hardware: Leverages historic math/algorithm strength. 🔸China Partnership: Emerging cooperation on military AI tech. The battlefield is Russia's AI lab. By integrating real-time combat data, they are creating a brutally effective feedback loop. This, combined with nascent military ai cooperation with China reflects Russia's dominance on the battlefield in current and future conflicts, as it has the most battle-tested and proven weaponry.
2:26

🚨Russia's Winning Military AI Race🇷🇺 Despite Western sanctions, Russia is making tangible, battlefield-proven progress in military AI. 🔸Ukrainian soldiers report Shahed drones now perform evasive maneuvers, likely using rear cameras & onboard AI to dodge interceptors. 🔸Lancet munitions & elite drone units use neural networks for precision strikes Russia's Playbook: 🔸Battlefield Feedback Loop: Direct data from Ukraine refines AI systems. 🔸Civilian Mobilization: Volunteers crowdsource data to train models. 🔸Software over Hardware: Leverages historic math/algorithm strength. 🔸China Partnership: Emerging cooperation on military AI tech. The battlefield is Russia's AI lab. By integrating real-time combat data, they are creating a brutally effective feedback loop. This, combined with nascent military ai cooperation with China reflects Russia's dominance on the battlefield in current and future conflicts, as it has the most battle-tested and proven weaponry.

NewRulesGeopolitics

10,246 просмотров • 8 месяцев назад

Billion-Dollar Data Centers Are Taking Over the World | Lauren Goode, WIRED When Sam Altman said one year ago that OpenAI’s Roman Empire is the actual Roman Empire, he wasn’t kidding. In the same way that the Romans gradually amassed an empire of land spanning three continents and one-ninth of the Earth’s circumference, the CEO and his cohort are now dotting the planet with their own latifundia—not agricultural estates, but AI data centers. Tech executives like Altman, Nvidia CEO Jensen Huang, Microsoft CEO Satya Nadella, and Oracle cofounder Larry Ellison are fully bought in to the idea that the future of the American (and possibly global) economy are these new warehouses stocked with IT infrastructure. But data centers, of course, aren’t actually new. In the earliest days of computing there were giant power-sucking mainframes in climate-controlled rooms, with co-ax cables moving information from the mainframe to a terminal computer. Then the consumer internet boom of the late 1990s spawned a new era of infrastructure. Massive buildings began popping up in the backyard of Washington, DC, with racks and racks of computers that stored and processed data for tech companies. A decade later, “the cloud” became the squishy infrastructure of the internet. Storage got cheaper. Some companies, like Amazon, capitalized on this. Giant data centers continued to proliferate, but instead of a tech company using some combination of on-premise servers and rented data center racks, they offloaded their computing needs to a bunch of virtualized environments. (“What is the cloud?” a perfectly intelligent family member asked me in the mid-2010s, “and why am I paying for 17 different subscriptions to it?”) All the while tech companies were hoovering up petabytes of data, data that people willingly shared online, in enterprise workspaces, and through mobile apps. Firms began finding new ways to mine and structure this “Big Data,” and promised that it would change lives. In many ways, it did. You had to know where this was going. Now the tech industry is in the fever-dream days of generative AI, which requires new levels of computing resources. Big Data is tired; big data centers are here, and wired—for AI. Faster, more efficient chips are needed to power AI data centers, and chipmakers like Nvidia and AMD have been jumping up and down on the proverbial couch, proclaiming their love for AI. The industry has entered an unprecedented era of capital investments in AI infrastructure, tilting the US into positive GDP territory. These are massive, swirling deals that might as well be cocktail party handshakes, greased with gigawatts and exuberance, while the rest of us try to track real contracts and dollars. OpenAI, Microsoft, Nvidia, Oracle, and SoftBank have struck some of the biggest deals. This year an earlier supercomputing project between OpenAI and Microsoft, called Stargate, became the vehicle for a massive AI infrastructure project in the US. (President Donald Trump called it the largest AI infrastructure project in history, because of course he did, but that may not have been hyperbolic.) Altman, Ellison, and SoftBank CEO Masayoshi Son were all in on the deal, pledging $100 billion to start, with plans to invest up to $500 billion into Stargate in the coming years. Nvidia GPUs would be deployed. Later, in July, OpenAI and Oracle announced an additional Stargate partnership—SoftBank curiously absent—measured in gigawatts of capacity (4.5) and expected job creation (around 100,000). Microsoft, Amazon, and Meta have also shared plans for multibillion-dollar data projects. Microsoft said at the start of 2025 that it was on track to invest “approximately $80 billion to build out AI-enabled data centers to train AI models and deploy AI and cloud-based applications around the world.” Then, in September, Nvidia said it would invest up to $100 billion in OpenAI, provided that OpenAI made good on a deal to use up to 10 gigawatts of Nvidia’s systems for OpenAI’s infrastructure plans, which means essentially that OpenAI has to pay Nvidia in order to get paid by Nvidia. The following month AMD said it would give OpenAI as much as 10 percent of the chip company if OpenAI purchased and deployed up to 6 gigawatts of AMD GPUs between now and 2030. It’s the circular nature of these investments that have the general public, and bearish analysts, wondering if we’re headed for an AI bubble burst. What’s clear is that the near-term downstream effects of these data center build-outs are real. The energy, resource, and labor demands of AI infrastructure are enormous. By some estimates, worldwide AI energy demand is set to surpass demand from bitcoin mining by the end of this year, WIRED has reported. The processors in data centers run hot and need to be cooled, so big tech companies are pulling from municipal water supplies to make that happen—and aren’t always disclosing how much water they’re using. Local wells are running dry or seem unsafe to drink from. Residents who live near data center construction sites are noting that traffic delays, and in some cases car crashes, are increasing. One corner of Richland Parish, Louisiana, home of Meta’s $27 billion Hyperion data center, has seen a 600 percent spike in vehicle crashes this year. Major proponents of AI seem to suggest that all of this will be worth it. Few top tech executives will publicly entertain the notion that this might be an overshoot, either ecologically or economically. “Emphatically … no,” Lisa Su, the chief executive of AMD, said earlier this month when asked if the AI froth has runneth over. Su, like other execs, cited overwhelming demand for AI as justification for these enormous capital expenditures. Demand from whom? Harder to pin down. In their mind, it’s everyone. All of us. The 800 million people who use ChatGPT on a weekly basis. The evolution from those 1990s data centers to the 2000s era of cloud computing to new AI data centers wasn’t just one continuum. The world has concurrently moved from the tiny internet to the big internet to the AI internet, and realistically speaking, there’s no going back. Generative AI is out of the bottle. The Sams and Jensens and Larrys and Lisas of the world aren’t wrong about this. It doesn’t mean they aren’t wrong about the math, though. About their economic predictions. Or their ideas about AI-powered productivity and the labor market. Or the availability of natural and material resources for these data centers. Or who will come once they build them. Or the timing of it all. Even Rome eventually collapsed.
2:23

Billion-Dollar Data Centers Are Taking Over the World | Lauren Goode, WIRED When Sam Altman said one year ago that OpenAI’s Roman Empire is the actual Roman Empire, he wasn’t kidding. In the same way that the Romans gradually amassed an empire of land spanning three continents and one-ninth of the Earth’s circumference, the CEO and his cohort are now dotting the planet with their own latifundia—not agricultural estates, but AI data centers. Tech executives like Altman, Nvidia CEO Jensen Huang, Microsoft CEO Satya Nadella, and Oracle cofounder Larry Ellison are fully bought in to the idea that the future of the American (and possibly global) economy are these new warehouses stocked with IT infrastructure. But data centers, of course, aren’t actually new. In the earliest days of computing there were giant power-sucking mainframes in climate-controlled rooms, with co-ax cables moving information from the mainframe to a terminal computer. Then the consumer internet boom of the late 1990s spawned a new era of infrastructure. Massive buildings began popping up in the backyard of Washington, DC, with racks and racks of computers that stored and processed data for tech companies. A decade later, “the cloud” became the squishy infrastructure of the internet. Storage got cheaper. Some companies, like Amazon, capitalized on this. Giant data centers continued to proliferate, but instead of a tech company using some combination of on-premise servers and rented data center racks, they offloaded their computing needs to a bunch of virtualized environments. (“What is the cloud?” a perfectly intelligent family member asked me in the mid-2010s, “and why am I paying for 17 different subscriptions to it?”) All the while tech companies were hoovering up petabytes of data, data that people willingly shared online, in enterprise workspaces, and through mobile apps. Firms began finding new ways to mine and structure this “Big Data,” and promised that it would change lives. In many ways, it did. You had to know where this was going. Now the tech industry is in the fever-dream days of generative AI, which requires new levels of computing resources. Big Data is tired; big data centers are here, and wired—for AI. Faster, more efficient chips are needed to power AI data centers, and chipmakers like Nvidia and AMD have been jumping up and down on the proverbial couch, proclaiming their love for AI. The industry has entered an unprecedented era of capital investments in AI infrastructure, tilting the US into positive GDP territory. These are massive, swirling deals that might as well be cocktail party handshakes, greased with gigawatts and exuberance, while the rest of us try to track real contracts and dollars. OpenAI, Microsoft, Nvidia, Oracle, and SoftBank have struck some of the biggest deals. This year an earlier supercomputing project between OpenAI and Microsoft, called Stargate, became the vehicle for a massive AI infrastructure project in the US. (President Donald Trump called it the largest AI infrastructure project in history, because of course he did, but that may not have been hyperbolic.) Altman, Ellison, and SoftBank CEO Masayoshi Son were all in on the deal, pledging $100 billion to start, with plans to invest up to $500 billion into Stargate in the coming years. Nvidia GPUs would be deployed. Later, in July, OpenAI and Oracle announced an additional Stargate partnership—SoftBank curiously absent—measured in gigawatts of capacity (4.5) and expected job creation (around 100,000). Microsoft, Amazon, and Meta have also shared plans for multibillion-dollar data projects. Microsoft said at the start of 2025 that it was on track to invest “approximately $80 billion to build out AI-enabled data centers to train AI models and deploy AI and cloud-based applications around the world.” Then, in September, Nvidia said it would invest up to $100 billion in OpenAI, provided that OpenAI made good on a deal to use up to 10 gigawatts of Nvidia’s systems for OpenAI’s infrastructure plans, which means essentially that OpenAI has to pay Nvidia in order to get paid by Nvidia. The following month AMD said it would give OpenAI as much as 10 percent of the chip company if OpenAI purchased and deployed up to 6 gigawatts of AMD GPUs between now and 2030. It’s the circular nature of these investments that have the general public, and bearish analysts, wondering if we’re headed for an AI bubble burst. What’s clear is that the near-term downstream effects of these data center build-outs are real. The energy, resource, and labor demands of AI infrastructure are enormous. By some estimates, worldwide AI energy demand is set to surpass demand from bitcoin mining by the end of this year, WIRED has reported. The processors in data centers run hot and need to be cooled, so big tech companies are pulling from municipal water supplies to make that happen—and aren’t always disclosing how much water they’re using. Local wells are running dry or seem unsafe to drink from. Residents who live near data center construction sites are noting that traffic delays, and in some cases car crashes, are increasing. One corner of Richland Parish, Louisiana, home of Meta’s $27 billion Hyperion data center, has seen a 600 percent spike in vehicle crashes this year. Major proponents of AI seem to suggest that all of this will be worth it. Few top tech executives will publicly entertain the notion that this might be an overshoot, either ecologically or economically. “Emphatically … no,” Lisa Su, the chief executive of AMD, said earlier this month when asked if the AI froth has runneth over. Su, like other execs, cited overwhelming demand for AI as justification for these enormous capital expenditures. Demand from whom? Harder to pin down. In their mind, it’s everyone. All of us. The 800 million people who use ChatGPT on a weekly basis. The evolution from those 1990s data centers to the 2000s era of cloud computing to new AI data centers wasn’t just one continuum. The world has concurrently moved from the tiny internet to the big internet to the AI internet, and realistically speaking, there’s no going back. Generative AI is out of the bottle. The Sams and Jensens and Larrys and Lisas of the world aren’t wrong about this. It doesn’t mean they aren’t wrong about the math, though. About their economic predictions. Or their ideas about AI-powered productivity and the labor market. Or the availability of natural and material resources for these data centers. Or who will come once they build them. Or the timing of it all. Even Rome eventually collapsed.

Owen Gregorian

55,427 просмотров • 7 месяцев назад

In the next 15 years, data centers are expected to add an additional $160 billion to grid costs in the US Estimate say electricity rates for average households will spike by as much as 70% Data centers are projected to triple their share of US electricity demand in the next few years The main driver is the explosive growth of data centers built by Big Tech companies like Amazon, Meta, Microsoft, Google, OpenAI and more to power artificial intelligence Places like Northern Virginia already has over 200 data centers with massive new ones planned. Utilities are striking secret proprietary deals with Big Tech companies. These are hidden behind NDAs that shift much of the infrastructure costs onto regular residential customers Just in the PJM energy market of 13 states covering 65 million people, data centers were responsible for 63% of last year’s record 800% spike in capacity prices (This is INSANE) Residential customers in places like Virginia and Louisiana are being forced to subsidize billions in new power plants and grid upgrades for data centers. An Examples of this is in Louisiana, Meta’s data center deal leaves the public potentially on the hook for half or more of a $3–4 billion power plant Again, without major policy changes, average household electricity bills could rise by up to 70% over the next 15 years due to data center demand. There is only one real way we can stop this, we must create a separate customer class for data centers Maryland and Oregon have already passed laws doing this Forces data centers to pay for the specific infrastructure they need instead of spreading the costs to everyone else. More states need to do the same Ban secret sweetheart deals Require full public disclosure of all contracts between utilities and Big Tech Prohibit deals where data centers pay below the actual cost of service Make data centers pay the full cost of new power plants and grid upgrades Change regulations so utilities cannot socialize the cost of data-center-driven infrastructure to residential and small business ratepayers This needs to be done immediately
13:33

In the next 15 years, data centers are expected to add an additional $160 billion to grid costs in the US Estimate say electricity rates for average households will spike by as much as 70% Data centers are projected to triple their share of US electricity demand in the next few years The main driver is the explosive growth of data centers built by Big Tech companies like Amazon, Meta, Microsoft, Google, OpenAI and more to power artificial intelligence Places like Northern Virginia already has over 200 data centers with massive new ones planned. Utilities are striking secret proprietary deals with Big Tech companies. These are hidden behind NDAs that shift much of the infrastructure costs onto regular residential customers Just in the PJM energy market of 13 states covering 65 million people, data centers were responsible for 63% of last year’s record 800% spike in capacity prices (This is INSANE) Residential customers in places like Virginia and Louisiana are being forced to subsidize billions in new power plants and grid upgrades for data centers. An Examples of this is in Louisiana, Meta’s data center deal leaves the public potentially on the hook for half or more of a $3–4 billion power plant Again, without major policy changes, average household electricity bills could rise by up to 70% over the next 15 years due to data center demand. There is only one real way we can stop this, we must create a separate customer class for data centers Maryland and Oregon have already passed laws doing this Forces data centers to pay for the specific infrastructure they need instead of spreading the costs to everyone else. More states need to do the same Ban secret sweetheart deals Require full public disclosure of all contracts between utilities and Big Tech Prohibit deals where data centers pay below the actual cost of service Make data centers pay the full cost of new power plants and grid upgrades Change regulations so utilities cannot socialize the cost of data-center-driven infrastructure to residential and small business ratepayers This needs to be done immediately

Wall Street Apes

57,720 просмотров • 2 месяцев назад

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.
1:39

"The limiting factor for AI deployment is fundamentally electrical power." That was Elon Musk in a conversation with Larry Fink during his first-ever appearance at Davos. And it's the most honest thing anyone in Big Tech has said in a while. Forget the hype about superintelligence and robots. Forget the promises about productivity gains that CFOs still can't measure. The bottleneck is power. And that bottleneck is very real. Here's why & how to position yourself to make the most out of this: We're producing more chips than we can turn on. AI chip production is increasing exponentially. US electricity generation is growing 3-4% annually. The math doesn't work. US data center power demand is expected to hit 75.8 gigawatts in 2026, up from 61.8 gigawatts in 2025. By 2030, it could reach 134 gigawatts. The largest US grid operator, PJM, expects to add 31 gigawatts of data center load over the next five years. But only 28 gigawatts of new generation capacity is planned. The deficit is already here. Residential electricity rates near data centers have jumped as much as 267% compared to five years ago. Regular Americans are subsidizing Silicon Valley's power consumption. Whether AI delivers on its big promises or not, the electricity bills are coming due right now. Elon made another point that deserves attention: China is solving the energy problem while America talks about AI miracles. He noted China's solar deployment is "tremendous." And that's an understatement. I verified the numbers. They're insane: China installed 275 gigawatts of solar capacity in the first 11 months of 2025. That's more than the ENTIRE installed solar capacity of the United States. In May 2025 alone, China added 93 gigawatts. Roughly 100 panels every second. For the first time in history, a single country surpassed 1,000 gigawatts of total solar capacity. China added more solar in one month than America has built in its entire history... But this ISN'T about climate policy. It's about strategic positioning. China understands something Wall Street is ignoring: whoever controls the energy infrastructure controls the next era of computing. While American investors chase the latest AI stock, China is building the power grid that will actually run the technology. My take: The data center buildout will fall short of expectations. Big Tech wants trillions in infrastructure. The grid can't deliver it. Not at this pace. Not with 3-4% annual electricity growth against exponential demand projections. Something has to give. Either the buildout slows dramatically, or electricity prices spike to levels that destroy the economics of the entire AI story. The AI trade has been built on the assumption that infrastructure will materialize to meet demand. That assumption is looking increasingly shaky. BUT the companies selling power to Big Tech win either way. Scarcity means pricing power. Utilities don't need AI to cure cancer. They just need hyperscalers to keep signing power agreements. And they are. Microsoft, Amazon, Meta, and Alphabet spent ~$350B in 2025 on data centers. That money flows to utilities and grid infrastructure whether AI changes the world or not. US utilities are forecasting a ~6% jump in capital expenditures to $228B in 2026. Cumulative utility capex is expected to surpass $1.1T through 2029. Dominion Energy is investing $50B through 2029, projecting 5-7% annual earnings growth. Entergy plans $41B between 2026 and 2029, targeting more than 8% compound annual earnings growth. These aren't speculative bets on AI changing everything... They're regulated utilities with contracted demand and predictable cash flows. So beware the AI story. The productivity miracle remains unproven. The physical constraints are becoming impossible to ignore. But the picks-and-shovels play? THAT'S where the smart money is looking. The hyperscalers have to pay their electricity bills. I'd rather own the companies collecting them.

George Noble

23,931 просмотров • 6 месяцев назад

🚨🇨🇳🇮🇷🇺🇸 China could be using Iran as a field laboratory for U.S. weapons Think about it: every U.S. and Israeli missile, drone, and fighter jet hitting Iran right now is an opportunity to be studied, dismantled, and reverse-engineered by Beijing. China's watching how Patriot systems, HIMARS, and Western tech perform in real combat, then feeding that data into their AI systems to find weaknesses. They're preparing for Taiwan and the South China Sea. They're also helping Iran replace Western software with encrypted Chinese systems like BeiDou (their GPS alternative) and providing intelligence on U.S. naval movements in the Gulf. China thinks the U.S. is stretched thin defending Israel and Ukraine, depleting military resources that would otherwise counter Chinese expansion in the Pacific. If this theory holds, every strike on Iran is training data for China's war plans. Source: Modern Diplomacy, mil3010 YT
0:23

🚨🇨🇳🇮🇷🇺🇸 China could be using Iran as a field laboratory for U.S. weapons Think about it: every U.S. and Israeli missile, drone, and fighter jet hitting Iran right now is an opportunity to be studied, dismantled, and reverse-engineered by Beijing. China's watching how Patriot systems, HIMARS, and Western tech perform in real combat, then feeding that data into their AI systems to find weaknesses. They're preparing for Taiwan and the South China Sea. They're also helping Iran replace Western software with encrypted Chinese systems like BeiDou (their GPS alternative) and providing intelligence on U.S. naval movements in the Gulf. China thinks the U.S. is stretched thin defending Israel and Ukraine, depleting military resources that would otherwise counter Chinese expansion in the Pacific. If this theory holds, every strike on Iran is training data for China's war plans. Source: Modern Diplomacy, mil3010 YT

Mario Nawfal

432,104 просмотров • 5 месяцев назад

On Tuesday, I testified before the House Homeland Security Committee on China's strides in robotics and AI. I warned that we lost solar, batteries, and EVs -- now we're at risk of losing robotics and AI. If that happens, it would irreversibly change the balance of power. Five points: 1️⃣ China aims to win the next industrial revolution. PRC leaders believe history is shaped by industrial revolutions. The first, steam power, made Britain dominant. The second and third, electrification and mass manufacturing, made America dominant. China is determined to win the fourth. 2️⃣ In robotics, China is already winning. In 2024, China installed 300,000 new industrial robots. America installed 30,000. China now has over 2 million robots in its factories — five times more than the US. A decade ago, it imported 75% of its robots. Today it makes 60% domestically. This year alone, China may spend $400 billion on industrial policy. The entire US CHIPS Act provided $50 billion across multiple years. If we fall behind here, U.S. reindustrialization becomes farfetched. 3️⃣ In AI, we're ahead — but selling off the advantage. China has more energy, more talent, and makes the edge devices. But America still leads because of chips, according to China's own AI companies. US chips are 4-5x better than China's today. We are debating whether to surrender that edge. 4️⃣ We are inviting risks of cyberespionage and catastrophic cyberattacks. PRC law requires its companies to cooperate with intelligence services and never disclose it. Today's robots carry LiDAR, microphones, and cameras — they are mobile surveillance platforms. But the bigger risk is cyberattack. We know China has compromised our power, gas, water, telecommunications, and transportation infrastructure in preparation for cyberattack. We cannot deploy robots in sensitive facilities from the very country targeting those facilities. 5️⃣ Here's what we must do. Extend ICTS rules to cover Chinese robots. Direct CISA to audit where they're deployed in critical infrastructure. Ban federal procurement of Chinese robotics and AI. Strengthen semiconductor export controls. Stop treating American AI companies with more regulatory scrutiny than Chinese ones. And build allied scale in robotics—a trading bloc with preferential terms for the members that can rival China's scale in in the sector. Thanks to @HomelandDemsIt and House Homeland GOP for the hearing on this topic, and grateful to join Michael Robbins and colleagues from Scale and Boston Dynamics for a great discussion.
5:03

On Tuesday, I testified before the House Homeland Security Committee on China's strides in robotics and AI. I warned that we lost solar, batteries, and EVs -- now we're at risk of losing robotics and AI. If that happens, it would irreversibly change the balance of power. Five points: 1️⃣ China aims to win the next industrial revolution. PRC leaders believe history is shaped by industrial revolutions. The first, steam power, made Britain dominant. The second and third, electrification and mass manufacturing, made America dominant. China is determined to win the fourth. 2️⃣ In robotics, China is already winning. In 2024, China installed 300,000 new industrial robots. America installed 30,000. China now has over 2 million robots in its factories — five times more than the US. A decade ago, it imported 75% of its robots. Today it makes 60% domestically. This year alone, China may spend $400 billion on industrial policy. The entire US CHIPS Act provided $50 billion across multiple years. If we fall behind here, U.S. reindustrialization becomes farfetched. 3️⃣ In AI, we're ahead — but selling off the advantage. China has more energy, more talent, and makes the edge devices. But America still leads because of chips, according to China's own AI companies. US chips are 4-5x better than China's today. We are debating whether to surrender that edge. 4️⃣ We are inviting risks of cyberespionage and catastrophic cyberattacks. PRC law requires its companies to cooperate with intelligence services and never disclose it. Today's robots carry LiDAR, microphones, and cameras — they are mobile surveillance platforms. But the bigger risk is cyberattack. We know China has compromised our power, gas, water, telecommunications, and transportation infrastructure in preparation for cyberattack. We cannot deploy robots in sensitive facilities from the very country targeting those facilities. 5️⃣ Here's what we must do. Extend ICTS rules to cover Chinese robots. Direct CISA to audit where they're deployed in critical infrastructure. Ban federal procurement of Chinese robotics and AI. Strengthen semiconductor export controls. Stop treating American AI companies with more regulatory scrutiny than Chinese ones. And build allied scale in robotics—a trading bloc with preferential terms for the members that can rival China's scale in in the sector. Thanks to @HomelandDemsIt and House Homeland GOP for the hearing on this topic, and grateful to join Michael Robbins and colleagues from Scale and Boston Dynamics for a great discussion.

Rush Doshi

152,123 просмотров • 4 месяцев назад

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside
2:43

Elon Musk explains the “series of miracles” xAI had to achieve to bring one gigawatt of power online for its supercomputer cluster in Memphis The xAI team coordinated dozens of gas turbines, faced permit challenges in Tennessee, and ultimately moved the power generation just across the border into Mississippi. From there, they had to run high-voltage transmission lines and build an entire power plant on the Mississippi side and it was very difficult to build that But the deeper point is even more important Most people calculate AI power needs by looking only at the GPUs. That is a serious mistake. A real data center also requires substantial power for networking, CPUs, storage, and most critically cooling In Memphis, where summer temperatures are extreme, peak cooling demand can add roughly 40% more power. On top of that, operators must maintain extra capacity so generators can be taken offline for servicing without shutting down the entire system. This redundancy typically adds another 20 to 25 percent When all of these factors are included, Elon stated that approximately 110,000 GB300 GPUs require roughly 330 megawatts of actual power generation This is why building and powering frontier AI data centers is far more difficult and far more capital intensive than it appears from the outside

X Freeze

140,274 просмотров • 3 месяцев назад