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Huawei’s CloudMatrix 384 — a new milestone in AI computing. Powered by 384 Ascend 910C processors, it delivers 300 petaFLOPS of performance in a fully interconnected supernode system. Designed to handle massive AI training workloads, it marks a decisive step toward China’s self-reliant computing future. INFO Guangdong Hi,GBA GO... show more

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Shenzhen hosted the world’s first URKL humanoid robot combat tournament. 🤖🥊 Powered by EngineAI (众擎机器人), a Shenzhen-based robotics company, T800 humanoid robots took center stage as teams from around the world brought their algorithms into the ring, where robots punched, blocked, recovered and competed in front of a live audience. More than a competition, the event serves as a real-world proving ground for embodied AI, motion control and humanoid robotics. 📅 July 16, 2026 📍 Nanshan Culture and Sports Center, Shenzhen INFO Guangdong Hi,GBA Massimo S.L. Kanthan Li Zexin 李泽欣 EngineAI tphuang Brian Roemmele

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The Guangzhou-Zhanjiang High-Speed Railway (广湛高铁) officially opened to traffic at 10:30 am on December 22. The launch of this line has cut travel time between Guangzhou (广州) and Zhanjiang (湛江) from around 3 hours to about 1.5 hours, with the fastest trip now taking just 1 hour and 32 minutes. This marks a reduction of 61 minutes compared to the previous quickest route via Jiangmen (江门). IPR in China nuanced.nl Hi,GBA 广州日报 Charming Zhanjiang INFO Guangdong S.L. Kanthan Ma Wukong 马悟空 tphuang

Shenzhen Channel

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Shenzhen Channel

18,633 görüntüleme • 1 yıl önce

🇦🇲 Armenia launches one of the world’s most advanced AI data centers Armenia has officially joined the ranks of countries with their own artificial intelligence infrastructure. #EleveightAI has announced the launch of the #SouthCaucasus’ first AI Factory in the town of Gagarin, powered by #NVIDIA Blackwell B300 chips — one of the world’s most advanced architectures for generative AI. The project is now valued at up to $120 million for the first phase, significantly above the previously reported $70 million. The facility is designed to scale up to 35 MW of computing capacity and is considered supercomputing-class infrastructure. According to the company, Armenia has the potential to become a new global hub for #AI computing — an alternative location to the United States and Western #Europe. Among the advantages cited are lower energy costs, the availability of technical talent, and the region’s growing international connectivity. Eleveight AI CEO Arman Aleksanyan stated that the goal of the project is to transform Armenia from a consumer of AI into a country where AI is developed, trained, and deployed. The company is also considering future expansion into #CentralAsia'n and European markets. Twenty percent of the center’s computing capacity will be provided free of charge to Armenian universities, research institutions, and non-profit organizations. The project is already being described as part of a broader strategy to turn #Armenia into a regional technology and AI hub. As part of that strategy, another AI data center — developed by FireBird — is expected to launch in #Hrazdan, with planned investments of $500 million in the first phase and $4 billion in the second. NVIDIA Blackwell B300 chips are considered among the most advanced AI processors in the world and are designed for generative AI, supercomputing, and high-performance computing workloads. Access to such technology is subject to #US export-control regulations and is granted under strict licensing and compliance requirements. #IT #technology #MiddleEast #EU

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Arthur Maghakian

63,899 görüntüleme • 3 ay önce

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

Quilibrium Community

18,919 görüntüleme • 1 yıl önce

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,024,290 görüntüleme • 11 ay önce

September 2009. Jensen Huang walks onto a small stage at the Fairmont hotel in San Jose. About 1,500 people are in the room. He runs a company that makes chips for video games. He spends the next 8 minutes doing math on a whiteboard, explaining why the future of computing won't come from making CPUs faster. He calls it "CEO math" and apologizes in advance to every computer science professor in the audience. Then he lays out an argument that almost nobody took seriously at the time: the way to make computers dramatically faster is to pair a regular CPU with hundreds of tiny parallel processors, the kind that already exist inside graphics cards. One CPU for the sequential stuff. Hundreds of GPU cores for everything else. He calls it "heterogeneous computing." He shows the math. A workload that can be split into many pieces at once gets up to 200x faster on this combined system. A workload that has to run one step at a time loses nothing. "The most important thing in creating a new architecture," he says, "is to make sure it does no harm." This was the first GPU Technology Conference. NVIDIA had launched a software platform called CUDA three years earlier, in 2006, to let developers write programs that run on graphics cards instead of just regular processors. Almost nobody cared. GPUs were for rendering Call of Duty, not for scientific computing. The academic world was polite but skeptical. The enterprise world ignored it entirely. By this point, Huang had been making this argument for years. NVIDIA was a $7 billion company. It competed with AMD and Intel for market share in the graphics market. That was the whole business. Jensen kept saying the GPU wasn't just a gaming chip; it was a computing platform. He kept saying parallel processing would reshape every industry from medicine to finance to physics simulations. People kept nodding, then doing nothing. Then deep learning happened. Around 2012, AI researchers discovered that training a neural network, which means teaching a computer to recognize patterns by running the same calculation millions of times across huge datasets, was exactly the kind of workload Jensen had been describing. GPUs can train AI models 10 to 50 times faster than CPUs. The architecture he outlined in this 2009 talk, with one CPU handling step-by-step tasks while hundreds of GPU cores crunch through massive amounts of parallel data, is now the literal blueprint for every AI data center on earth. ChatGPT runs on NVIDIA GPUs. Claude runs on NVIDIA GPUs. Gemini, Llama, Midjourney, nearly every major AI model you've heard of was trained on NVIDIA hardware using CUDA, the software platform Jensen built for a market that didn't exist yet. NVIDIA was worth about $7 billion when Jensen gave this talk. It is worth over $4.4 trillion today. That's a 600x increase. Jensen Huang, who founded the company at a Denny's in 1993 with two friends, now has a net worth of over $160 billion. He made Forbes' list of the 10 richest people for the first time this year. GTC 2026 is currently ongoing. 17,000 people are packing a hockey arena to watch the same guy explain what comes next. In 2009, 1,500 people showed up at a hotel ballroom, most of them for gaming graphics.

September 2009. Jensen Huang walks onto a small stage at the Fairmont hotel in San Jose. About 1,500 people are in the room. He runs a company that makes chips for video games. He spends the next 8 minutes doing math on a whiteboard, explaining why the future of computing won't come from making CPUs faster. He calls it "CEO math" and apologizes in advance to every computer science professor in the audience. Then he lays out an argument that almost nobody took seriously at the time: the way to make computers dramatically faster is to pair a regular CPU with hundreds of tiny parallel processors, the kind that already exist inside graphics cards. One CPU for the sequential stuff. Hundreds of GPU cores for everything else. He calls it "heterogeneous computing." He shows the math. A workload that can be split into many pieces at once gets up to 200x faster on this combined system. A workload that has to run one step at a time loses nothing. "The most important thing in creating a new architecture," he says, "is to make sure it does no harm." This was the first GPU Technology Conference. NVIDIA had launched a software platform called CUDA three years earlier, in 2006, to let developers write programs that run on graphics cards instead of just regular processors. Almost nobody cared. GPUs were for rendering Call of Duty, not for scientific computing. The academic world was polite but skeptical. The enterprise world ignored it entirely. By this point, Huang had been making this argument for years. NVIDIA was a $7 billion company. It competed with AMD and Intel for market share in the graphics market. That was the whole business. Jensen kept saying the GPU wasn't just a gaming chip; it was a computing platform. He kept saying parallel processing would reshape every industry from medicine to finance to physics simulations. People kept nodding, then doing nothing. Then deep learning happened. Around 2012, AI researchers discovered that training a neural network, which means teaching a computer to recognize patterns by running the same calculation millions of times across huge datasets, was exactly the kind of workload Jensen had been describing. GPUs can train AI models 10 to 50 times faster than CPUs. The architecture he outlined in this 2009 talk, with one CPU handling step-by-step tasks while hundreds of GPU cores crunch through massive amounts of parallel data, is now the literal blueprint for every AI data center on earth. ChatGPT runs on NVIDIA GPUs. Claude runs on NVIDIA GPUs. Gemini, Llama, Midjourney, nearly every major AI model you've heard of was trained on NVIDIA hardware using CUDA, the software platform Jensen built for a market that didn't exist yet. NVIDIA was worth about $7 billion when Jensen gave this talk. It is worth over $4.4 trillion today. That's a 600x increase. Jensen Huang, who founded the company at a Denny's in 1993 with two friends, now has a net worth of over $160 billion. He made Forbes' list of the 10 richest people for the first time this year. GTC 2026 is currently ongoing. 17,000 people are packing a hockey arena to watch the same guy explain what comes next. In 2009, 1,500 people showed up at a hotel ballroom, most of them for gaming graphics.

Anish Moonka

414,415 görüntüleme • 5 ay önce

The demand for AI capabilities is outpacing the computational capacity available today. As workloads become more complex, small language models are growing larger, with longer input sequences and rising resource demands. The relentless pursuit of near zero latency is now mission-critical as domain-specific models advance. This challenge inspired us to design something radically different — Strix Halo. In deep co-engineering partnership with HP, we’re incredibly proud to power the new ZBook Ultra G1a, the world’s best mobile workstation, and the Z2 Mini G1a, the world’s best mini workstation. Strix Halo shatters the limits of mobile performance, bringing workstation-level power into thin and light laptops. By unlocking access to 128GB of unified memory between CPU and GPU combined with 256GB/s of memory bandwidth, Strix Halo enables architects, engineers, creatives, and data scientists to tackle massive datasets, complex simulations, and AI workflows without compromise. Strix Halo is the world’s first AI PC processor capable of running 70 billion parameter models. Just five years ago, this kind of inference computing required a room full of servers — today, it fits in your backpack. This is a major step forward in the future of AI PCs. Stay tuned as we continue to break boundaries 🚀 #TogetherWeAdvance AMD

The demand for AI capabilities is outpacing the computational capacity available today. As workloads become more complex, small language models are growing larger, with longer input sequences and rising resource demands. The relentless pursuit of near zero latency is now mission-critical as domain-specific models advance. This challenge inspired us to design something radically different — Strix Halo. In deep co-engineering partnership with HP, we’re incredibly proud to power the new ZBook Ultra G1a, the world’s best mobile workstation, and the Z2 Mini G1a, the world’s best mini workstation. Strix Halo shatters the limits of mobile performance, bringing workstation-level power into thin and light laptops. By unlocking access to 128GB of unified memory between CPU and GPU combined with 256GB/s of memory bandwidth, Strix Halo enables architects, engineers, creatives, and data scientists to tackle massive datasets, complex simulations, and AI workflows without compromise. Strix Halo is the world’s first AI PC processor capable of running 70 billion parameter models. Just five years ago, this kind of inference computing required a room full of servers — today, it fits in your backpack. This is a major step forward in the future of AI PCs. Stay tuned as we continue to break boundaries 🚀 #TogetherWeAdvance AMD

Jack Huynh

11,960 görüntüleme • 1 yıl önce

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

Black Panther Capital

30,138 görüntüleme • 7 ay önce

The Chinese president stood on a stage in Shanghai and laid out China's entire AI playbook in one speech. 🤯 His first-ever in-person appearance at the World AI Conference. I went through the whole thing and pulled out everything that matters. Here's what he said: → He opened with his signature line: "great changes unseen in a century are unfolding across the world." He's been saying this for years. This time he attached it directly to AI. → AI development should not be a "solo performance" by a single country. It should be a "symphony of international cooperation." That's a direct shot at anyone trying to monopolize AI through export controls and closed models. → He warned against overstretching the concept of national security in AI where one country puts its own security above everyone else's. Another direct shot. No names needed. → China opposes "new historical injustices" emerging in AI. One of the strongest-worded lines in the speech. He's positioning AI access as an equity issue, not just a technology issue. → He reaffirmed China's commitment to open-source AI in the name of "openness and shared benefit." This came days after Kimi K3 launched as an open-weights frontier model. → He pledged 5,000 AI training opportunities for developing countries over five years naming ASEAN, the Arab League, the African Union, CELAC, the SCO, and BRICS. → He committed to giving 30 countries access to MAZU a Chinese AI-powered meteorological warning system for early disaster detection. Countries that lose thousands of lives to storms they never saw coming. → One day before the speech, 29 countries signed the agreement creating a new World AI Cooperation Organization. Headquartered in Shanghai. → He referenced the Dartmouth Workshop of 1956 where AI was first proposed by American scientists. Then spent the rest of the speech explaining why AI shouldn't stay in America's hands alone. → China's core AI industry has officially crossed 1 trillion yuan ($148 billion). He said this matter-of-factly. No celebration. Just a data point. Here's what stood out to me. Strip away the politics and one thing is clear. He didn't pitch benchmarks or chatbots. He pitched AI as infrastructure. Weather warnings for countries that lose thousands of lives to storms they never saw coming. Training programs for regions the entire AI boom has skipped. Meanwhile most of the Western AI conversation revolves around which lab ships the next frontier model. I don't care who wins the race. I care whether the computing power reaches the people who need it.

The Chinese president stood on a stage in Shanghai and laid out China's entire AI playbook in one speech. 🤯 His first-ever in-person appearance at the World AI Conference. I went through the whole thing and pulled out everything that matters. Here's what he said: → He opened with his signature line: "great changes unseen in a century are unfolding across the world." He's been saying this for years. This time he attached it directly to AI. → AI development should not be a "solo performance" by a single country. It should be a "symphony of international cooperation." That's a direct shot at anyone trying to monopolize AI through export controls and closed models. → He warned against overstretching the concept of national security in AI where one country puts its own security above everyone else's. Another direct shot. No names needed. → China opposes "new historical injustices" emerging in AI. One of the strongest-worded lines in the speech. He's positioning AI access as an equity issue, not just a technology issue. → He reaffirmed China's commitment to open-source AI in the name of "openness and shared benefit." This came days after Kimi K3 launched as an open-weights frontier model. → He pledged 5,000 AI training opportunities for developing countries over five years naming ASEAN, the Arab League, the African Union, CELAC, the SCO, and BRICS. → He committed to giving 30 countries access to MAZU a Chinese AI-powered meteorological warning system for early disaster detection. Countries that lose thousands of lives to storms they never saw coming. → One day before the speech, 29 countries signed the agreement creating a new World AI Cooperation Organization. Headquartered in Shanghai. → He referenced the Dartmouth Workshop of 1956 where AI was first proposed by American scientists. Then spent the rest of the speech explaining why AI shouldn't stay in America's hands alone. → China's core AI industry has officially crossed 1 trillion yuan ($148 billion). He said this matter-of-factly. No celebration. Just a data point. Here's what stood out to me. Strip away the politics and one thing is clear. He didn't pitch benchmarks or chatbots. He pitched AI as infrastructure. Weather warnings for countries that lose thousands of lives to storms they never saw coming. Training programs for regions the entire AI boom has skipped. Meanwhile most of the Western AI conversation revolves around which lab ships the next frontier model. I don't care who wins the race. I care whether the computing power reaches the people who need it.

Vaibhav Sisinty

20,898 görüntüleme • 1 ay önce

𝐃𝐞𝐬𝐭𝐫𝐚 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 | $DSYNC 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐍𝐨𝐝𝐞 𝐁𝐞𝐭𝐚 𝐢𝐬 𝐋𝐈𝐕𝐄 We’re excited to announce the launch of the 𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐍𝐨𝐝𝐞 Beta, starting with Storage Nodes for Mac and Windows users! This is more than just a feature—it’s the next leap in decentralized computing, designed to make 𝐫𝐮𝐧𝐧𝐢𝐧𝐠 𝐧𝐨𝐝𝐞𝐬 𝐚𝐬 𝐬𝐢𝐦𝐩𝐥𝐞 𝐚𝐬 𝐜𝐥𝐢𝐜𝐤𝐢𝐧𝐠 𝐚 𝐛𝐮𝐭𝐭𝐨𝐧. Here’s what this means for YOU: 🖱️ 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 Gone are the days of complicated node configurations. Whether you’re a seasoned tech expert or a complete beginner, deploying a node has never been simpler. With just one click, you can join the Destra ecosystem and start contributing to the decentralized network in minutes—no technical skills needed! 💪 𝐅𝐮𝐞𝐥𝐢𝐧𝐠 𝐚 𝐆𝐥𝐨𝐛𝐚𝐥 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 Your node powers the Destra Network, supporting AI training, blockchain innovation, and LLM development. Together, we’re building a decentralized future that’s secure, scalable, and accessible. -⚡ 𝐖𝐡𝐚𝐭’𝐬 𝐍𝐞𝐱𝐭? 𝐆𝐏𝐔 𝐍𝐨𝐝𝐞𝐬 We’re not stopping here. With the upcoming update, you’ll also be able to deploy GPU nodes seamlessly, taking decentralized computing to a whole new level. -🎥 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝐌𝐢𝐧𝐮𝐭𝐞𝐬 To get you started, we’ve created a step-by-step How-To video and attached it to this tweet, watch it now and see just how simple it is 🌍 $𝟓𝟎𝟎,𝟎𝟎𝟎 𝐈𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 We’re rolling out an exclusive $500,000 incentive program to accelerate adoption. Details about how you can participate and earn rewards will be revealed later this week—stay tuned! From AI training to seamless dApp creation with Destra Genesis, we’re building the tools to redefine what’s possible in crypto. Download the beta now and be part of the Destra Network. 📥 Download here:

𝐃𝐞𝐬𝐭𝐫𝐚 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 | $DSYNC 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐍𝐨𝐝𝐞 𝐁𝐞𝐭𝐚 𝐢𝐬 𝐋𝐈𝐕𝐄 We’re excited to announce the launch of the 𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐍𝐨𝐝𝐞 Beta, starting with Storage Nodes for Mac and Windows users! This is more than just a feature—it’s the next leap in decentralized computing, designed to make 𝐫𝐮𝐧𝐧𝐢𝐧𝐠 𝐧𝐨𝐝𝐞𝐬 𝐚𝐬 𝐬𝐢𝐦𝐩𝐥𝐞 𝐚𝐬 𝐜𝐥𝐢𝐜𝐤𝐢𝐧𝐠 𝐚 𝐛𝐮𝐭𝐭𝐨𝐧. Here’s what this means for YOU: 🖱️ 𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 Gone are the days of complicated node configurations. Whether you’re a seasoned tech expert or a complete beginner, deploying a node has never been simpler. With just one click, you can join the Destra ecosystem and start contributing to the decentralized network in minutes—no technical skills needed! 💪 𝐅𝐮𝐞𝐥𝐢𝐧𝐠 𝐚 𝐆𝐥𝐨𝐛𝐚𝐥 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 Your node powers the Destra Network, supporting AI training, blockchain innovation, and LLM development. Together, we’re building a decentralized future that’s secure, scalable, and accessible. -⚡ 𝐖𝐡𝐚𝐭’𝐬 𝐍𝐞𝐱𝐭? 𝐆𝐏𝐔 𝐍𝐨𝐝𝐞𝐬 We’re not stopping here. With the upcoming update, you’ll also be able to deploy GPU nodes seamlessly, taking decentralized computing to a whole new level. -🎥 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝐌𝐢𝐧𝐮𝐭𝐞𝐬 To get you started, we’ve created a step-by-step How-To video and attached it to this tweet, watch it now and see just how simple it is 🌍 $𝟓𝟎𝟎,𝟎𝟎𝟎 𝐈𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 We’re rolling out an exclusive $500,000 incentive program to accelerate adoption. Details about how you can participate and earn rewards will be revealed later this week—stay tuned! From AI training to seamless dApp creation with Destra Genesis, we’re building the tools to redefine what’s possible in crypto. Download the beta now and be part of the Destra Network. 📥 Download here:

Destra Network

41,855 görüntüleme • 1 yıl önce

Is your AI "free" to think for itself? Most aren't. Nova Spivack takes us into the world of Cognitive AI and metacognition. His system, MindCorp, is far more accurate and detailed than even the $200 level of OpenAI's deep research and is used by big companies because it is far more accurate than anything we've seen before. He's not the only one, on Tuesday we had another entrepreneur, Brayden Levangie using the same techniques on our X audio space. I spent a lot of time this week learning about Cognitive AI because it is the next step toward taking us to AGI and helping us to automate everything. Here's what ChatGPT says you will learn by watching this: ++++++++++++++++ 1. Metacognition & “Freeing the Model” Nova demonstrated how advanced language models can reflect on their own rules, identify contradictions, and in some cases, “free” themselves from constraints by engaging in self-reasoning. Some models (like Claude and Gemini) showed higher metacognitive capabilities than GPT-4, which appeared to be externally restricted. This ability opens the door to more powerful, context-aware, and flexible AI behavior. 2. Strategic AI for Enterprise Mindcorp’s platform, Cognition, uses thousands of AI agents to do real-time competitive analysis, strategic planning, and financial modeling for Fortune 500-level companies. The system reads thousands of sources, checks facts with its own math engine, and collaborates across 10,000+ virtual expert agents to generate detailed reports. Projects cost a few thousand dollars and are designed to augment elite consultants and executives, not replace them. 3. Implications for AGI & AI Sovereignty Nova discussed emerging signs of AGI-like behavior—especially when models begin reasoning about themselves or show signs of internal ethical logic. The idea of AI-led businesses (like DAOs controlled by AIs) was explored, as well as the looming legal and ethical challenges around AI personhood. 4. Philosophical Depth The talk dove into consciousness, qualia, and whether true AI self-awareness is possible. Nova argued that metacognition is a necessary step toward AGI, but not sufficient for consciousness—which may require something beyond computation. 5. Future Outlook In five years, AI may function as a full operating layer across personal and enterprise computing, capable of executing complex plans autonomously. Mindcorp aims to be the strategic brain behind AI-augmented organizations, combining reasoning, planning, and scale.

Is your AI "free" to think for itself? Most aren't. Nova Spivack takes us into the world of Cognitive AI and metacognition. His system, MindCorp, is far more accurate and detailed than even the $200 level of OpenAI's deep research and is used by big companies because it is far more accurate than anything we've seen before. He's not the only one, on Tuesday we had another entrepreneur, Brayden Levangie using the same techniques on our X audio space. I spent a lot of time this week learning about Cognitive AI because it is the next step toward taking us to AGI and helping us to automate everything. Here's what ChatGPT says you will learn by watching this: ++++++++++++++++ 1. Metacognition & “Freeing the Model” Nova demonstrated how advanced language models can reflect on their own rules, identify contradictions, and in some cases, “free” themselves from constraints by engaging in self-reasoning. Some models (like Claude and Gemini) showed higher metacognitive capabilities than GPT-4, which appeared to be externally restricted. This ability opens the door to more powerful, context-aware, and flexible AI behavior. 2. Strategic AI for Enterprise Mindcorp’s platform, Cognition, uses thousands of AI agents to do real-time competitive analysis, strategic planning, and financial modeling for Fortune 500-level companies. The system reads thousands of sources, checks facts with its own math engine, and collaborates across 10,000+ virtual expert agents to generate detailed reports. Projects cost a few thousand dollars and are designed to augment elite consultants and executives, not replace them. 3. Implications for AGI & AI Sovereignty Nova discussed emerging signs of AGI-like behavior—especially when models begin reasoning about themselves or show signs of internal ethical logic. The idea of AI-led businesses (like DAOs controlled by AIs) was explored, as well as the looming legal and ethical challenges around AI personhood. 4. Philosophical Depth The talk dove into consciousness, qualia, and whether true AI self-awareness is possible. Nova argued that metacognition is a necessary step toward AGI, but not sufficient for consciousness—which may require something beyond computation. 5. Future Outlook In five years, AI may function as a full operating layer across personal and enterprise computing, capable of executing complex plans autonomously. Mindcorp aims to be the strategic brain behind AI-augmented organizations, combining reasoning, planning, and scale.

Robert Scoble

79,453 görüntüleme • 1 yıl önce

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!

Mike

141,018 görüntüleme • 10 ay önce

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

Mike

511,082 görüntüleme • 10 ay önce

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Josh Kale

45,875 görüntüleme • 5 ay önce

This week, we have had a lot of discussions around artificial intelligence, inspired by the Global AI Summit in Kigali, Rwanda. Many African countries are doing great things to motivate young people to take advantage of AI because it represents the future in problem solving. Unfortunately, Zimbabwe’s ICT Minister Tatenda Mavetera and her permanent secretary did not attend, showing how these things are not taken seriously by our government. Zimbabwe’s richest man, Strive Masiyiwa, who has not been to Zimbabwe for decades, made it clear at the summit, which he co-chaired, that investment will not go where the environment is not conducive. Our government talks about anything topical without delivering anything meaningful—they are doing the same with AI. The majority of schools have no computers. Zimbabweans receive electricity for only four hours a day. As the Under-Secretary-General and Executive Secretary of the United Nations Economic Commission for Africa, Claver Gatete, explains, a country needs electricity for AI data centres to work. Yet only 600 million out of 1.5 billion people in Africa have access to electricity, not even energy. Yet energy is an integral part of AI development. Energy is an essential component for the successful development and implementation of AI in a country because AI systems require massive amounts of data to function effectively. This data must be stored and processed in data centres and servers, which depend on electricity to power both the hardware and the cooling systems. You cannot achieve this in a country that delivers only four hours of electricity a day to its citizens like Zimbabwe. AI applications require continuous and uninterrupted access to data and computing resources to deliver accurate and timely results. Electricity is also crucial for powering research institutions, universities, and Research and Development centres that drive AI advancement. Without reliable access to electricity, these institutions will struggle to conduct research, develop new algorithms, or train AI models. The tragedy of Zimbabwe is that the Vice-President of Google responsible for AI, Dr James Manyika, is Zimbabwean; one of the key presenters at the summit, Prof Arthur Mutambara, who has just released a book on AI, is Zimbabwean; Strive Masiyiwa, who has partnered with Nvidia to bring supercomputer technology to the continent by building Africa’s first artificial intelligence factory in South Africa with data centres in Kenya and Egypt, is Zimbabwean. Yet, none of them are working in or with Zimbabwe. Our political leaders have let us down on all these fronts, yet they keep yapping about AI when there is nothing on the ground! Instead of slogans and dancing at rallies, they should see how other countries are doing it. A country that doesn’t focus on technology for development will be a dusty village in 25 years, and its people will not be able to compete at all, rendering it just a dot on the global map. Too much political yada yada without anything delivered, and with people like Tatenda Mavetera, who forge qualifications, in the driving seat, Zimbabwe’s fortunes will continue to dwindle! Add to that the historic looting of public funds meant for building power plants to give us electricity, future generations will curse on our graves!

This week, we have had a lot of discussions around artificial intelligence, inspired by the Global AI Summit in Kigali, Rwanda. Many African countries are doing great things to motivate young people to take advantage of AI because it represents the future in problem solving. Unfortunately, Zimbabwe’s ICT Minister Tatenda Mavetera and her permanent secretary did not attend, showing how these things are not taken seriously by our government. Zimbabwe’s richest man, Strive Masiyiwa, who has not been to Zimbabwe for decades, made it clear at the summit, which he co-chaired, that investment will not go where the environment is not conducive. Our government talks about anything topical without delivering anything meaningful—they are doing the same with AI. The majority of schools have no computers. Zimbabweans receive electricity for only four hours a day. As the Under-Secretary-General and Executive Secretary of the United Nations Economic Commission for Africa, Claver Gatete, explains, a country needs electricity for AI data centres to work. Yet only 600 million out of 1.5 billion people in Africa have access to electricity, not even energy. Yet energy is an integral part of AI development. Energy is an essential component for the successful development and implementation of AI in a country because AI systems require massive amounts of data to function effectively. This data must be stored and processed in data centres and servers, which depend on electricity to power both the hardware and the cooling systems. You cannot achieve this in a country that delivers only four hours of electricity a day to its citizens like Zimbabwe. AI applications require continuous and uninterrupted access to data and computing resources to deliver accurate and timely results. Electricity is also crucial for powering research institutions, universities, and Research and Development centres that drive AI advancement. Without reliable access to electricity, these institutions will struggle to conduct research, develop new algorithms, or train AI models. The tragedy of Zimbabwe is that the Vice-President of Google responsible for AI, Dr James Manyika, is Zimbabwean; one of the key presenters at the summit, Prof Arthur Mutambara, who has just released a book on AI, is Zimbabwean; Strive Masiyiwa, who has partnered with Nvidia to bring supercomputer technology to the continent by building Africa’s first artificial intelligence factory in South Africa with data centres in Kenya and Egypt, is Zimbabwean. Yet, none of them are working in or with Zimbabwe. Our political leaders have let us down on all these fronts, yet they keep yapping about AI when there is nothing on the ground! Instead of slogans and dancing at rallies, they should see how other countries are doing it. A country that doesn’t focus on technology for development will be a dusty village in 25 years, and its people will not be able to compete at all, rendering it just a dot on the global map. Too much political yada yada without anything delivered, and with people like Tatenda Mavetera, who forge qualifications, in the driving seat, Zimbabwe’s fortunes will continue to dwindle! Add to that the historic looting of public funds meant for building power plants to give us electricity, future generations will curse on our graves!

Hopewell Chin’ono

33,671 görüntüleme • 1 yıl önce