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Qualcomm is launching its next-Gen top-tier flagship Snapdragon SoCs for mobile platform. This time, the company is launching two 8 Elite SoCs instead of one, with both manufactured on TSMCs 2nm process on September 22, 2026. 🔳 Snapdragon 8 Elite Gen 6 🔳 Snapdragon 8 Elite Gen 6 Pro...

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Abhishek Yadav

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25,660 views • 4 days ago •via X (Twitter)

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The next generation of mobile experiences is here. #Snapdragon 8 Elite features the fastest mobile CPU in the world thanks to the Qualcomm Oryon CPU, paired with the most powerful on-device #AI ever for a smartphone.
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The next generation of mobile experiences is here. #Snapdragon 8 Elite features the fastest mobile CPU in the world thanks to the Qualcomm Oryon CPU, paired with the most powerful on-device #AI ever for a smartphone.

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This isn’t a chat overlay, it’s a true multimodal agent. It has the brand-new Snapdragon 8 Elite Gen 5 with 16GB RAM, so it can push a lot of the agentic workload on-device. Here I take a picture of a NIO battery swap station and ask, “What is this thing?” It’s running ByteDance’s Doubao model (>175M users in China): a massive, sparse MoE model with full text+vision support. It recognizes the infrastructure from the photo, grounds it to NIO’s network, and explains what it does.
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This isn’t a chat overlay, it’s a true multimodal agent. It has the brand-new Snapdragon 8 Elite Gen 5 with 16GB RAM, so it can push a lot of the agentic workload on-device. Here I take a picture of a NIO battery swap station and ask, “What is this thing?” It’s running ByteDance’s Doubao model (>175M users in China): a massive, sparse MoE model with full text+vision support. It recognizes the infrastructure from the photo, grounds it to NIO’s network, and explains what it does.

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iQOO showcases a prototype of its 10th anniversary device designed based on fans' demands. Launch is not confirmed yet. Sharing specifications doesn’t make sense, but still sharing: 📱 7-inch 2K Samsung M16 full-screen display without punch hole, LTPO 6.0, 240Hz refresh rate ⬛️ Snapdragon 8 Elite Gen 10 Pro chip alongside iQOO’s own QX gaming chip 📸 200MP main Samsung HPC + 50MP ultrawide + 200MP telephoto periscope rear camera 🔋 15,000mAh solid-state battery 🫆 3D ultrasonic in-display fingerprint scanner covering half the display

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Abhishek Yadav

16,213 views • 7 months ago

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.
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Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.

NEXA AI

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VIDEO | On RSS chief Mohan Bhagwat's August 6 interaction with Gen Z and Gen Alpha, I.I.M.U.N Founder Rishabh Shah says, "This may be new for many people, but our organisation has been working for the past 15 years. Every year, our culmination event is held in August, between August 1 and August 15. Throughout the year, we organise programmes across India and in several countries for children and young people aged 11 to 19. Today, this age group is referred to as Gen Z and Gen Alpha. The four-day event includes debates and discussions for Gen Z and Gen Alpha participants. On the first day, we host keynote speakers, while the second, third and fourth days are dedicated to debates on local, national and international issues. The same format is followed across 108 cities in India and 15 countries, with the concluding event taking place in August. So, there is nothing unusual about holding it at this time. I understand why this has become such a major topic now, because the discussion is centred around Gen Z. It is also important to note that all 2,000 participants at the event belong to Gen Z. On whether Mohan Bhagwat will attend physically or virtually, Shah adds, "He will attend the event in person. We had previously invited RSS chief Mohan Bhagwat in 2016 and again in 2020 to interact with young people, guide them and share his views, and he attended on those occasions as well. Many prominent personalities from different fields have addressed our platform over the years. Rahul Gandhi has also interacted with the leadership team of I.I.M.U.N. The idea behind this platform is to carry the spirit of Bharatiyata (Indianness) to the next generation..."

Press Trust of India

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This is Imran Chaudhry and his wife Bethany. Both of them are former employees of Apple. Imran Chaudhry was a designer at Apple. He worked at Apple for 20 years. He designed many iPads, iPhones, and accessories. Bethany was responsible for all of the iOS projects. The duo founded their company Humane five years ago. Yesterday, they launched their first product and blew up the technology world. Their product is called a mobile phone replacement. Humane has created a wearable AI assistant that will do all the work with your smartphones called “Humane AI Pin”. It has a camera and mic and a projector. You give it commands by speaking, you can call it, send messages, and it can also translate your call live. Its laser projector will display it on your hand. You can check the time with it if you are busy. It will also reply to your calls and emails. It will also take care of your health and food intake. You will see what you have eaten in front of it and it will tell you whether it is good for your health or not. A few multinational companies have invested 200 million dollars in Imran Chaudhry’s company Humane, including Microsoft and LG Mobiles. Humane's device has a Qualcomm chip, and Humane's investors include Qualcomm. After launching this device, Humane is being called the killer of Apple and iPhone. Humane has priced it at $700 and will start taking orders on November 16.
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This is Imran Chaudhry and his wife Bethany. Both of them are former employees of Apple. Imran Chaudhry was a designer at Apple. He worked at Apple for 20 years. He designed many iPads, iPhones, and accessories. Bethany was responsible for all of the iOS projects. The duo founded their company Humane five years ago. Yesterday, they launched their first product and blew up the technology world. Their product is called a mobile phone replacement. Humane has created a wearable AI assistant that will do all the work with your smartphones called “Humane AI Pin”. It has a camera and mic and a projector. You give it commands by speaking, you can call it, send messages, and it can also translate your call live. Its laser projector will display it on your hand. You can check the time with it if you are busy. It will also reply to your calls and emails. It will also take care of your health and food intake. You will see what you have eaten in front of it and it will tell you whether it is good for your health or not. A few multinational companies have invested 200 million dollars in Imran Chaudhry’s company Humane, including Microsoft and LG Mobiles. Humane's device has a Qualcomm chip, and Humane's investors include Qualcomm. After launching this device, Humane is being called the killer of Apple and iPhone. Humane has priced it at $700 and will start taking orders on November 16.

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Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.
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Jensen Huang, CEO of Nvidia, is telling you where to invest in 2026. He has personally directed Nvidia's capital into 8 specific companies for a combined total of over $45 BILLION. This is where the most important company in the AI economy is putting its money. Here’s the full list: OpenAI: $30 billion The largest commitment of the 8. Nvidia is funding the buildout of OpenAI's compute infrastructure from the inside. OpenAI is also Nvidia's single largest customer. GLW Corning: $3.2 billion Optical glass and fiber to physically connect AI clusters. You cannot move data between millions of GPUs without it. IREN: $2.1 billion AI cloud provider with one of the deepest power positions in North America. MRVL Marvell: $2 billion Custom networking chips that move data between GPUs at massive scale. LITE Lumentum: $2 billion Lasers and optical components for the fiber backbone of every AI data center. COHR Coherent: $2 billion Fiber optic transceivers that connect GPU clusters inside data centers. CRWV CoreWeave: $2 billion GPU-as-a-service provider. Nvidia's largest cloud customer outside the hyperscalers. NBIS Nebius: $2 billion AI cloud infrastructure company. Quietly building hyperscale GPU capacity for the AI labs. Whatever Nvidia is buying is where the money is going next. At The Assembly, we’re a team of 8 with one goal: help you find the right stocks early. Turn notifications on so you don’t miss our alerts. This is VERY important. If you’re not following us yet, you will regret it later.

The Assembly

7,292,012 views • 3 months ago

$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!
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$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!

Mike

43,219 views • 7 months ago

Attention all citizens of the world! We are excited to announce that in just 20 days, the Perfect World will be launching! This revolutionary project is set to change the way we experience gaming and virtual reality forever! The Perfect World project will be launched on multiple launchpads, multi chains and will be listed on top exchanges, making it accessible to gamers and investors alike. With this multi-platform launch, the project is set to be the talk of the town, and everyone is eagerly anticipating its release. The Perfect World project is not just any ordinary game. It's an immersive, never-ending adventure that allows players to become anyone or anything they desire. With state-of-the-art AI technologies, players can create their characters based on their unique preferences and interests. They can then explore a vast open world, complete challenges, and even create their own games using the AI-powered imagination engine. To make this project even more exciting, the Perfect World team is partnering with some of the most prominent names in the industry. We are collaborating with top gaming studios, blockchain developers, and AI experts to create the ultimate gaming experience. We invite you to join us on this incredible journey as we explore the limitless possibilities of the Perfect World. Get ready to experience gaming like never before, with the perfect blend of innovation, creativity, and technology. Stay tuned for more updates on this groundbreaking project, and get ready to enter the perfect world! $PA #GameFi #AI #PerfectWorld #DAO
0:40

Attention all citizens of the world! We are excited to announce that in just 20 days, the Perfect World will be launching! This revolutionary project is set to change the way we experience gaming and virtual reality forever! The Perfect World project will be launched on multiple launchpads, multi chains and will be listed on top exchanges, making it accessible to gamers and investors alike. With this multi-platform launch, the project is set to be the talk of the town, and everyone is eagerly anticipating its release. The Perfect World project is not just any ordinary game. It's an immersive, never-ending adventure that allows players to become anyone or anything they desire. With state-of-the-art AI technologies, players can create their characters based on their unique preferences and interests. They can then explore a vast open world, complete challenges, and even create their own games using the AI-powered imagination engine. To make this project even more exciting, the Perfect World team is partnering with some of the most prominent names in the industry. We are collaborating with top gaming studios, blockchain developers, and AI experts to create the ultimate gaming experience. We invite you to join us on this incredible journey as we explore the limitless possibilities of the Perfect World. Get ready to experience gaming like never before, with the perfect blend of innovation, creativity, and technology. Stay tuned for more updates on this groundbreaking project, and get ready to enter the perfect world! $PA #GameFi #AI #PerfectWorld #DAO

Perfect World

372,081 views • 3 years ago

🚨🇩🇪 GERMANY JUST ADDED 8 MASSIVE WARSHIPS TO ITS NAVAL FLEET Germany is going all in on naval power, expanding its F127 frigate order from 6 to 8 ships. This isn't just another naval upgrade; they’re next-gen air defense warships loaded with radar tech and missile systems designed to control the seas. The total price tag? A cool $30.6 billion. That’s more than the GDP of some countries, all for a fleet that hasn’t even hit the water yet. Looks like Germany isn’t waiting to find out if Trump’s serious about leaving NATO slackers to fend for themselves. Source: Defence Index
3:02

🚨🇩🇪 GERMANY JUST ADDED 8 MASSIVE WARSHIPS TO ITS NAVAL FLEET Germany is going all in on naval power, expanding its F127 frigate order from 6 to 8 ships. This isn't just another naval upgrade; they’re next-gen air defense warships loaded with radar tech and missile systems designed to control the seas. The total price tag? A cool $30.6 billion. That’s more than the GDP of some countries, all for a fleet that hasn’t even hit the water yet. Looks like Germany isn’t waiting to find out if Trump’s serious about leaving NATO slackers to fend for themselves. Source: Defence Index

Mario Nawfal

98,760 views • 10 months ago

DELL ASUS ACER AND GIGABYTE ALL SHIPPED $2,999 DGX SPARK CLONES IN THE SAME QUARTER, SAMSUNG GEN 5 SSD CHOICE CAUSES 2X PERFORMANCE GAPS, THE LOCAL AI HARDWARE MAP JUST GREW BY FOUR FRONTIER TIER OPTIONS 02:13 the reviewer flips through the spec sheets, "we saw major differences in the SSD, some are using Gen 4 SSDs which will slow these platforms down, others have Gen 5 Samsung drives, Samsung is the leader by almost a factor of two" NVIDIA's GB10 Grace Blackwell reference board got picked up by Dell, Asus, Acer and Gigabyte in the same quarter, four $2,999 mini boxes with 128GB unified memory and 1 PFLOP of AI throughput from competing OEMs the Asus version runs coolest across GPU, CPU and SSD thanks to an unpainted bottom plate and a copper heatsink element, the Acer leads on price-to-perf, the Dell honeycomb intake actually restricts airflow slightly, the gigabyte sits middle of pack Samsung Gen 5 versus Gen 4 inside the same chassis causes a 2x read-write gap for the exact same $2,999 sticker, the buyer who reads the SSD spec sheet gets a 70B model loading in 4 seconds, the one who doesn't waits 8 your serie's frontier tier was the $1,700 EVO-X2, four DGX Spark OEM clones at $2,999 extend the map upward, the local AI hardware boom is now staffed by every major PC vendor not just AMD and apple, the category fully arrived bookmark this and read the article below
2:50

DELL ASUS ACER AND GIGABYTE ALL SHIPPED $2,999 DGX SPARK CLONES IN THE SAME QUARTER, SAMSUNG GEN 5 SSD CHOICE CAUSES 2X PERFORMANCE GAPS, THE LOCAL AI HARDWARE MAP JUST GREW BY FOUR FRONTIER TIER OPTIONS 02:13 the reviewer flips through the spec sheets, "we saw major differences in the SSD, some are using Gen 4 SSDs which will slow these platforms down, others have Gen 5 Samsung drives, Samsung is the leader by almost a factor of two" NVIDIA's GB10 Grace Blackwell reference board got picked up by Dell, Asus, Acer and Gigabyte in the same quarter, four $2,999 mini boxes with 128GB unified memory and 1 PFLOP of AI throughput from competing OEMs the Asus version runs coolest across GPU, CPU and SSD thanks to an unpainted bottom plate and a copper heatsink element, the Acer leads on price-to-perf, the Dell honeycomb intake actually restricts airflow slightly, the gigabyte sits middle of pack Samsung Gen 5 versus Gen 4 inside the same chassis causes a 2x read-write gap for the exact same $2,999 sticker, the buyer who reads the SSD spec sheet gets a 70B model loading in 4 seconds, the one who doesn't waits 8 your serie's frontier tier was the $1,700 EVO-X2, four DGX Spark OEM clones at $2,999 extend the map upward, the local AI hardware boom is now staffed by every major PC vendor not just AMD and apple, the category fully arrived bookmark this and read the article below

starmex

17,573 views • 1 month ago

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
2:49

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 views • 5 months ago

Stop begging a model for magic. Start directing. Koyal is an agentic AI filmmaking platform It turns a script - or even raw audio - into a film you can cut, recut, and release It starts where filmmakers start: performance Then agents do what a crew would: scenes, shots, retakes Multilingual script → audio → video Agentic lip-sync with believable motion Characters that stay themselves across scenes Because real directors don’t “prompt” a movie They write. Perform. Reshoot. Koyal brings AI to that century-old workflow 🎙️ Mehul Agarwal, Co-founder & CEO koyal.ai (YC F25) on Fondo.com Start 00:00 Intro to Mehul and Koyal “Agentic AI filmmaking platform” 01:10 Most exciting videos on the platform 01:34 Music video and filming at a UNESCO site with AI 02:55 Origin story from text-to-3D gaming startup to AI filmmaking 03:24 The early days of Stable Diffusion, Midjourney, and the AI image explosion 04:22 Why the gaming industry is a "thankless" place for startups 04:50 "why isn't this a startup?" moment 05:15 meme pages, standup comedy, and scriptwriting 07:08 How Koyal works: the "Pixar process" without a camera 08:30 Launching the first video hackathon at YC 10:47 Where to follow and try Koyal
13:09

Stop begging a model for magic. Start directing. Koyal is an agentic AI filmmaking platform It turns a script - or even raw audio - into a film you can cut, recut, and release It starts where filmmakers start: performance Then agents do what a crew would: scenes, shots, retakes Multilingual script → audio → video Agentic lip-sync with believable motion Characters that stay themselves across scenes Because real directors don’t “prompt” a movie They write. Perform. Reshoot. Koyal brings AI to that century-old workflow 🎙️ Mehul Agarwal, Co-founder & CEO koyal.ai (YC F25) on Fondo.com Start 00:00 Intro to Mehul and Koyal “Agentic AI filmmaking platform” 01:10 Most exciting videos on the platform 01:34 Music video and filming at a UNESCO site with AI 02:55 Origin story from text-to-3D gaming startup to AI filmmaking 03:24 The early days of Stable Diffusion, Midjourney, and the AI image explosion 04:22 Why the gaming industry is a "thankless" place for startups 04:50 "why isn't this a startup?" moment 05:15 meme pages, standup comedy, and scriptwriting 07:08 How Koyal works: the "Pixar process" without a camera 08:30 Launching the first video hackathon at YC 10:47 Where to follow and try Koyal

David J Phillips

25,753 views • 6 months ago

$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.
0:49

$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 views • 10 months ago

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.
0:45

🚨 WE’RE NOT BULLISH ENOUGH! 56% of my portfolio is in $IREN, $CIFR, $NBIS, $TE = AI/HPC, GRID & ENERGY. Spend 45 seconds watching this clip, and you’ll know why. 1. $CIFR has an elite team of ex-hyperscaler specialists from $GOOG, $META, and $AAPL optimizing every site. 2. The next couple of years will see more acquisitions, more sites, more deals in AI data centers. 3. $IREN secured a $9.7B AI cloud contract with $MSFT. $CIFR inked a $5.5B lease with $AMZN AWS and has a stake from $GOOG. 4. $NBIS landed $3B with $META and multi-billion with $MSFT. 5. J.P. Morgan forecasts AI infrastructure capex for data centers hitting $1.4 trillion annually by 2030, tripling near-term levels. 6. Jon Gray, President and COO of Blackstone Inc., stated in his latest presentation: “It starts with the picks and shovels, with things like chips, data centers, and power at massive scale. We have really focused on the right two circles here.” (Refering to data centers and power) 7. $NVDA’s Jensen Huang: “We wish we had more compute energy.” 8. The current administration pushes AI, energy, and defense with plans for massive power plants to fuel the AI boom. Onwards, -BP Not financial advice. Turn on notifications 🔔 for alerts on new posts.

Black Panther Capital

58,387 views • 7 months ago