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How Data Management Fuels Fujitsu’s AI Breakthroughs by Ronald van Loon & Eiji Ikeda | #QlikPartner Qlik #QlikConnect #AI #GenAI #Data #MachineLearning #DigitalTransformation #Productivity #Technology Cc: Pascal Bornet | Yves Mulkers | Linda Grasso | Glen Gilmore |

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Ronald van Loon

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13,754 views • 1 year ago •via X (Twitter)

Science & Technology#QlikPartner#QlikConnect#AI#GenAI#Data#MachineLearning#DigitalTransformation#Productivity#Technology
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Ronald van Loon

10,558 views • 1 year ago

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Ronald van Loon

12,667 views • 1 year ago

It is an #AI-powered solution that monitors unsafe acts by Interesting Engineering #AI #ArtificialIntelligence #Tech #Innovation #EmergingTech #FutureOfWork cc: 🚴‍♀️ AnneGuzman - 🇨🇦 John C. Maxwell, III Ronald van Loon Pascal Bornet
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It is an #AI-powered solution that monitors unsafe acts by Interesting Engineering #AI #ArtificialIntelligence #Tech #Innovation #EmergingTech #FutureOfWork cc: 🚴‍♀️ AnneGuzman - 🇨🇦 John C. Maxwell, III Ronald van Loon Pascal Bornet

Ronald van Loon

13,103 views • 3 years ago

AI-Powered Campus Networks: Insights on University #Tech Transformations by Ronald van Loon & Sudheer Matta | Learn more: #JuniperNetworksPartner @JuniperNetworks #AI #Data #UserExperience #Education #Networking #Innovation Cc: Dr. Marcell Vollmer #StaySafe #CES2026 @FrRonconi
2:02

AI-Powered Campus Networks: Insights on University #Tech Transformations by Ronald van Loon & Sudheer Matta | Learn more: #JuniperNetworksPartner @JuniperNetworks #AI #Data #UserExperience #Education #Networking #Innovation Cc: Dr. Marcell Vollmer #StaySafe #CES2026 @FrRonconi

Ronald van Loon

20,942 views • 3 years ago

🏆 The New Year 2025 arrived with another big recognition for me – I was named among the Top 10 Tech Influencers to Follow in 2025 by @insightdottech❕ 🔝🔟👥 Thank you very much, @insightdottech! You can find more information in my #blog post: 👇🏽 👏🏽 Congratulations to the fellow tech influencers in the list Top 10 Tech Influencers to Follow in 2025: Antonio Grasso, Glen Gilmore, Harold Sinnott #MWC26, Kevin L. Jackson, Kirk Borne, Linda Grasso, Dr. Monika Sonu I Founder Healthinnovationtoolbox, Peggy Smedley and Ronald van Loon 🙌🏽 Looking forward to new exciting partnerships in 2025! 🤝🏽💼 📌 Read the full list Top 10 Tech Influencers to Follow in 2025 at: 🔗➡️ ℹ️ Sponsored by Intel, @insightdottech is a global publication that showcases members of the #Intel® Partner Alliance ecosystem. #Technology #Tech #TechTrends #TechTrends2025 #EmergingTech #FutureTech #TechInfluencer #Influencers #ThoughtLeadership #Leadership #InfluencerMarketing #Marketing #SocialMedia #Innovation #AI #ArtificialIntelligence #GenAI #ML #IoT #IIoT #CyberSecurity #Cloud #FutureofWork #DigitalTransformation #Industry40 #RPA #Robotics #Robots #Web3 #5G #AR #VR #DataScience #BigData #Analytics #CX #Business #TechForGood #Sustainability #WomenInTech #WomenInSTEM
0:20

🏆 The New Year 2025 arrived with another big recognition for me – I was named among the Top 10 Tech Influencers to Follow in 2025 by @insightdottech❕ 🔝🔟👥 Thank you very much, @insightdottech! You can find more information in my #blog post: 👇🏽 👏🏽 Congratulations to the fellow tech influencers in the list Top 10 Tech Influencers to Follow in 2025: Antonio Grasso, Glen Gilmore, Harold Sinnott #MWC26, Kevin L. Jackson, Kirk Borne, Linda Grasso, Dr. Monika Sonu I Founder Healthinnovationtoolbox, Peggy Smedley and Ronald van Loon 🙌🏽 Looking forward to new exciting partnerships in 2025! 🤝🏽💼 📌 Read the full list Top 10 Tech Influencers to Follow in 2025 at: 🔗➡️ ℹ️ Sponsored by Intel, @insightdottech is a global publication that showcases members of the #Intel® Partner Alliance ecosystem. #Technology #Tech #TechTrends #TechTrends2025 #EmergingTech #FutureTech #TechInfluencer #Influencers #ThoughtLeadership #Leadership #InfluencerMarketing #Marketing #SocialMedia #Innovation #AI #ArtificialIntelligence #GenAI #ML #IoT #IIoT #CyberSecurity #Cloud #FutureofWork #DigitalTransformation #Industry40 #RPA #Robotics #Robots #Web3 #5G #AR #VR #DataScience #BigData #Analytics #CX #Business #TechForGood #Sustainability #WomenInTech #WomenInSTEM

Elitsa Krumova

21,330 views • 1 year ago

💥🏢Watch the Applications of Generative #AI in Architecture👌 شاهد تطبيقات الذكاء💥👌 الاصطناعي التوليدي في الهندسة المعمارية TY/Ronald van Loon #innovation #AI #tech #IoT #Web3 #GenAI #GenerativeAI #technology #MachineLearning #EmergingTech #marketing #ArtificialIntelligence #MI #Robotics #smartcities #digital #تقنية #التحول_الرقمي #الذكاء_الاصطناعي #الذكاء_الاصطناعي_التوليدي #الذكاء_الصناعي_السعودي Eveline Ruehlin Knut Jägersberg Tyson Lester, MBA, ChHC®, REBC®, RHU® Glen Gilmore Chidambara .ML. Olivier Laborde#CES2026 #Peace 🇫🇷🇪🇺🇺🇦 Tony Moroney Trevor Lloyd-Jones Nicolas Babin Jean CAYEUX (Hour CET) Lionel Costes Pinna Pierre Pierre Cappelli 🌏 Pierre Chamard Jérôme MONANGE  Thierry Pires 🇫🇷 @vanivina9 Maia Gabunia amal.merzouk ⴰⵎⴰⵍ ⵎⴻⵔⵣoⵓⴽ Neus Lorenzo Diane Kānêza #CES2026 Mary Gambara Mario Xavier ANCELIN  Dr Stephen Harwood #Tech4Good #SDG🇺🇦#CES2026 Eric Gaubert Anthony Rochand Ene Sharon Boxer Smaksked Skåne AB 🌐smaksked.bsky.social Dev Khanna @LavaletteAstrid Andres Vilariño 🇪🇦 Franco Ronconi 🇮🇹 LoFrano 🇧🇷 Greg Valancius Estrella BusinessIntelligence Dinis Guarda @fogle_shane Françoise Morvan Nafis Alam Elitsa Krumova MHcommunicate #Mastodon👉@mhcommunicate@social Eduardo Valente Adel Al Ammadi Dr. Theophano Mitsa ☦️🇬🇷🇺🇸 Jean-Baptiste Lefevre Alain GOUDEY
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💥🏢Watch the Applications of Generative #AI in Architecture👌 شاهد تطبيقات الذكاء💥👌 الاصطناعي التوليدي في الهندسة المعمارية TY/Ronald van Loon #innovation #AI #tech #IoT #Web3 #GenAI #GenerativeAI #technology #MachineLearning #EmergingTech #marketing #ArtificialIntelligence #MI #Robotics #smartcities #digital #تقنية #التحول_الرقمي #الذكاء_الاصطناعي #الذكاء_الاصطناعي_التوليدي #الذكاء_الصناعي_السعودي Eveline Ruehlin Knut Jägersberg Tyson Lester, MBA, ChHC®, REBC®, RHU® Glen Gilmore Chidambara .ML. Olivier Laborde#CES2026 #Peace 🇫🇷🇪🇺🇺🇦 Tony Moroney Trevor Lloyd-Jones Nicolas Babin Jean CAYEUX (Hour CET) Lionel Costes Pinna Pierre Pierre Cappelli 🌏 Pierre Chamard Jérôme MONANGE  Thierry Pires 🇫🇷 @vanivina9 Maia Gabunia amal.merzouk ⴰⵎⴰⵍ ⵎⴻⵔⵣoⵓⴽ Neus Lorenzo Diane Kānêza #CES2026 Mary Gambara Mario Xavier ANCELIN  Dr Stephen Harwood #Tech4Good #SDG🇺🇦#CES2026 Eric Gaubert Anthony Rochand Ene Sharon Boxer Smaksked Skåne AB 🌐smaksked.bsky.social Dev Khanna @LavaletteAstrid Andres Vilariño 🇪🇦 Franco Ronconi 🇮🇹 LoFrano 🇧🇷 Greg Valancius Estrella BusinessIntelligence Dinis Guarda @fogle_shane Françoise Morvan Nafis Alam Elitsa Krumova MHcommunicate #Mastodon👉@mhcommunicate@social Eduardo Valente Adel Al Ammadi Dr. Theophano Mitsa ☦️🇬🇷🇺🇸 Jean-Baptiste Lefevre Alain GOUDEY

Dr. Khulood Almani | د.خلود المانع

79,199 views • 2 years ago

Leonardo da Vinci AI Resurrected I have the pleasure to present one of my life passions to bring Leonardo da Vinci to life with AI. And I am excited to have done this public in a very special event - the The Chicago AI Week under the curation and production of Xiaochen Zhang that has nurtured a human first community. Leonardo da Vinci AI brings the legendary genius, polymath to life, by deploying a proprietary hashtag#AI LLM and conversational AI created by AI.DNA, part of ztudium - creator - The Chicago AI Week is a high quality opportunity to engage with major leaders, organisations, corporations such as the City of Chicago, Google, AWS, Microsoft, the US Federal Reserve System, at the forefront of leading AI innovation / management. - Chicago AI Week organiser hashtag#AI2030 is a global initiative dedicated to harnessing AI’s transformative power for humanity’s betterment. - Leonardo da Vinci AI agent assistant is a technology solution that bridges history and technology with a realistic HumanAI Generative model. It is a part of the upcoming - an AI platform that blends wisdom to accelerate the wise use of AI for education with edutainment technology. The CHICAGO AI WEEK 2024, from June 25–28, is a very special event that joins like minded stellar lineup of global luminaries in AI leadership and enterprise personalities and organisations. The lineup includes renowned figures such as Jessica Yeats, Sunaina Tuteja, Meghna Sinha, Jessica Yeats, Xiaochen Zhang. Artificial Intelligence (AI) and Spatial Computing, are redefining our Human to Human, Human to Machines and even Machine to Machine augmented Interface experience but also opening doors to unprecedented possibilities, and transforming the very fabric of our virtual and social communities existence. The reflection of AI and its impact is important to be considered in the context of history and how we discuss and research the multiple possibilities and create this important tech. The eternal Renaissance super genius, inventor, painting, artists, Polymath: Leonardo is a great way to rethink how we can look at AI. CC Helen Yu Antonio Vieira Santos arlene newbigging Elise Quevedo #WomenInTech #Innovation Dr Efi Pylarinou Enrico Molinari #VivaTech2025 Kohei Kurihara - Privacy for all together 🌍 Jean-Baptiste Lefevre Thomas Power Eveline Ruehlin Dr. Khulood Almani | د.خلود المانع Spiros Margaris Dr. Marcell Vollmer #StaySafe #CES2026 Xavier Gomez Antonio Grasso Estrella Sen. Sally Eaves Alvin Foo Urs Bolt 🇨🇭🇵🇭 ALBERTO GARUCCIO Tony Moroney #DigitalTransformation Mike de Waal Theodora (Theo) Lau - 劉䂀曼 🌻 💙 #TechForGood 💙 @FrRonconi Thinkers360 Ronald van Loon ipfconline
2:27

Leonardo da Vinci AI Resurrected I have the pleasure to present one of my life passions to bring Leonardo da Vinci to life with AI. And I am excited to have done this public in a very special event - the The Chicago AI Week under the curation and production of Xiaochen Zhang that has nurtured a human first community. Leonardo da Vinci AI brings the legendary genius, polymath to life, by deploying a proprietary hashtag#AI LLM and conversational AI created by AI.DNA, part of ztudium - creator - The Chicago AI Week is a high quality opportunity to engage with major leaders, organisations, corporations such as the City of Chicago, Google, AWS, Microsoft, the US Federal Reserve System, at the forefront of leading AI innovation / management. - Chicago AI Week organiser hashtag#AI2030 is a global initiative dedicated to harnessing AI’s transformative power for humanity’s betterment. - Leonardo da Vinci AI agent assistant is a technology solution that bridges history and technology with a realistic HumanAI Generative model. It is a part of the upcoming - an AI platform that blends wisdom to accelerate the wise use of AI for education with edutainment technology. The CHICAGO AI WEEK 2024, from June 25–28, is a very special event that joins like minded stellar lineup of global luminaries in AI leadership and enterprise personalities and organisations. The lineup includes renowned figures such as Jessica Yeats, Sunaina Tuteja, Meghna Sinha, Jessica Yeats, Xiaochen Zhang. Artificial Intelligence (AI) and Spatial Computing, are redefining our Human to Human, Human to Machines and even Machine to Machine augmented Interface experience but also opening doors to unprecedented possibilities, and transforming the very fabric of our virtual and social communities existence. The reflection of AI and its impact is important to be considered in the context of history and how we discuss and research the multiple possibilities and create this important tech. The eternal Renaissance super genius, inventor, painting, artists, Polymath: Leonardo is a great way to rethink how we can look at AI. CC Helen Yu Antonio Vieira Santos arlene newbigging Elise Quevedo #WomenInTech #Innovation Dr Efi Pylarinou Enrico Molinari #VivaTech2025 Kohei Kurihara - Privacy for all together 🌍 Jean-Baptiste Lefevre Thomas Power Eveline Ruehlin Dr. Khulood Almani | د.خلود المانع Spiros Margaris Dr. Marcell Vollmer #StaySafe #CES2026 Xavier Gomez Antonio Grasso Estrella Sen. Sally Eaves Alvin Foo Urs Bolt 🇨🇭🇵🇭 ALBERTO GARUCCIO Tony Moroney #DigitalTransformation Mike de Waal Theodora (Theo) Lau - 劉䂀曼 🌻 💙 #TechForGood 💙 @FrRonconi Thinkers360 Ronald van Loon ipfconline

Dinis Guarda

20,618 views • 2 years ago

‘Catch Me Up’ #GenAI #Tennis Summaries: #Wimbledon and IBM new digital experience IBM watsonx technology for sports: Generative AI use cases for enhanced customer engagement and experiences at Wimbledon The All England Lawn Tennis Club (AELTC) and IBM are expanding their use of generative artificial intelligence (Gen AI) at this year’s Wimbledon, using the technology to create automated, personalised catchup summaries across the championships, Wimbledon’s digital channels. ‘Catch Me Up’ generates pre- and post-match player cards with AI-generated player stories containing key statistics and match highlights, as well as longer-form daily summaries of the day’s action. Gen AI was first introduced at last year’s tournament when it was used to create automated spoken word commentary for its highlights, and the AELTC and IBM believe the technology can help them cover more matches and serve diverse audiences, complementing their existing coverage. “So, this service is offering a very quick and easy way to come in and pick up on what those key storylines are that are relevant to you and to make sure that you’re aware of what’s going on. The top seed and the show courts, of course, get a lot of coverage. But there’s a lot of tennis going on across 18 courts over the two weeks, which means not all the stories get told,” added Kevin Farrar, head of sports partnerships at IBM and the company’s lead for the Wimbledon partnership. CC Helen Yu Antonio Vieira Santos arlene newbigging Elise Quevedo #WomenInTech #Innovation Dr Efi Pylarinou Enrico Molinari #VivaTech2025 Kohei Kurihara - Privacy for all together 🌍 Jean-Baptiste Lefevre Thomas Power Eveline Ruehlin Dr. Khulood Almani | د.خلود المانع Spiros Margaris Dr. Marcell Vollmer #StaySafe #CES2026 Xavier Gomez Antonio Grasso Estrella Sen. Sally Eaves Alvin Foo Urs Bolt 🇨🇭🇵🇭 ALBERTO GARUCCIO Tony Moroney #DigitalTransformation Mike de Waal Theodora (Theo) Lau - 劉䂀曼 🌻 💙 #TechForGood 💙 Franco Ronconi 🇮🇹 Thinkers360 Ronald van Loon ipfconline
0:28

‘Catch Me Up’ #GenAI #Tennis Summaries: #Wimbledon and IBM new digital experience IBM watsonx technology for sports: Generative AI use cases for enhanced customer engagement and experiences at Wimbledon The All England Lawn Tennis Club (AELTC) and IBM are expanding their use of generative artificial intelligence (Gen AI) at this year’s Wimbledon, using the technology to create automated, personalised catchup summaries across the championships, Wimbledon’s digital channels. ‘Catch Me Up’ generates pre- and post-match player cards with AI-generated player stories containing key statistics and match highlights, as well as longer-form daily summaries of the day’s action. Gen AI was first introduced at last year’s tournament when it was used to create automated spoken word commentary for its highlights, and the AELTC and IBM believe the technology can help them cover more matches and serve diverse audiences, complementing their existing coverage. “So, this service is offering a very quick and easy way to come in and pick up on what those key storylines are that are relevant to you and to make sure that you’re aware of what’s going on. The top seed and the show courts, of course, get a lot of coverage. But there’s a lot of tennis going on across 18 courts over the two weeks, which means not all the stories get told,” added Kevin Farrar, head of sports partnerships at IBM and the company’s lead for the Wimbledon partnership. CC Helen Yu Antonio Vieira Santos arlene newbigging Elise Quevedo #WomenInTech #Innovation Dr Efi Pylarinou Enrico Molinari #VivaTech2025 Kohei Kurihara - Privacy for all together 🌍 Jean-Baptiste Lefevre Thomas Power Eveline Ruehlin Dr. Khulood Almani | د.خلود المانع Spiros Margaris Dr. Marcell Vollmer #StaySafe #CES2026 Xavier Gomez Antonio Grasso Estrella Sen. Sally Eaves Alvin Foo Urs Bolt 🇨🇭🇵🇭 ALBERTO GARUCCIO Tony Moroney #DigitalTransformation Mike de Waal Theodora (Theo) Lau - 劉䂀曼 🌻 💙 #TechForGood 💙 Franco Ronconi 🇮🇹 Thinkers360 Ronald van Loon ipfconline

Dinis Guarda

12,360 views • 2 years ago

Dwarkesh is WRONG about the "Output Gap" The narrative around Artificial Intelligence has shifted perceptibly in late 2025. After years of exponential hype, a sense of disillusionment has begun to settle over the industry. Commentators and analysts, most notably podcaster and writer Dwarkesh Patel, have recently highlighted what is being called the “Output Gap.” This is the uncomfortable discrepancy between our models’ skyrocketing performance on benchmarks and the relatively stagnant growth in macroeconomic productivity. We have reached “superhuman” capability on tests, yet global GDP hasn’t skyrocketed, and the promised revolution feels curiously delayed. This frustration stems from a fundamental misunderstanding of where we are in the technology cycle. The industry is currently fixated on “Day 0” capabilities—the raw intelligence of the models, the scaling laws, and the saturation of academic benchmarks. However, the bottleneck has shifted. We are no longer limited by the intelligence of the model, but by the inertia of the enterprise. The “Output Gap” is not a failure of technology; it is a lag in organizational digestion. We have invented a powerful jet engine, but we are essentially frustrated that it hasn’t revolutionized travel before we’ve even built the airframe to mount it on. The primary fallacy driving this disappointment is the expectation of the “drop-in remote worker.” Many observers equate Artificial General Intelligence (AGI) with a digital human that can be onboarded, culturally assimilated, and left to run autonomously with minimal supervision. Because current agents cannot seamlessly replace a human employee in this one-to-one fashion, the conclusion is often that the technology has stalled. This view misses the forest for the trees. Disruptive technologies rarely act as direct replacements; instead, they require a complete restructuring of how work is done. In reality, the barrier to adoption is what IT professionals call “Day 2 Operations.” Day 0 is the exciting launch; Day 2 is the boring, messy reality of governance, security, and maintenance. For an enterprise to deploy an autonomous agent, it isn’t enough for the model to be smart. The organization must solve for Role-Based Access Control (RBAC), SOC 2 compliance, liability frameworks, and data sovereignty. Right now, most organizations lack the infrastructure to handle “non-human” identities that have access to sensitive corporate data. Consider the security implications. A human employee has physical limitations and a single identity. An AI agent is an “always-on” entity that, if given improper permissions, could theoretically read every email in a company server to “optimize workflow.” Security teams (CISOs) are rightly terrified of this prospect. Until we develop granular access controls specifically designed for agents—effectively an “RBAC for AI”—the widespread deployment of autonomous agents will remain blocked by the “Departments of No”: Legal, HR, and Security. This operational friction explains why we are seeing a massive divergence between individual and enterprise adoption. Individually, adoption is rampant; we are in the “Shadow IT” era of AI, where employees secretly use tools like ChatGPT to boost their personal productivity. However, at the organizational level, adoption is glacial because the institution’s primary mandate is risk management, not speed. The C-suite is asking questions about ROI, liability, and data leakage that the current software ecosystem cannot yet answer satisfactorily. History offers a comforting precedent for this timeline. We are currently effectively in the “2002 era” of virtualization. In the early 2000s, virtualization technology (like VMware) was technically viable, but it took nearly a decade to become the default enterprise standard. It required years of maturing management software, security protocols, and cultural shifts before “The Cloud” became a reality. AI is undergoing the same digestion period. The technology is ready, but the enterprise “rails” required to run it are still being laid. This leads us to the “Mechanical Horse” fallacy. When the automobile was invented, people didn’t need a mechanical horse that walked on four legs; they needed a car. But the car required asphalt roads, not dirt paths. Similarly, we are currently trying to force AI into human-shaped workflows (”jobs”) rather than rebuilding the workflows to suit the AI. We are waiting for the mechanical horse, failing to realize that the true disruption comes from unbundling the work entirely. A “job” is essentially a bundle of tasks, context, and responsibilities aggregated for a single human. AI does not replace jobs; it unbundles tasks. The transition we are navigating involves breaking down these bundles and determining which specific value streams can be automated by agents. This requires task decomposition and new management frameworks—a “Council of AIs” approach—rather than a single omnipotent bot. This restructuring is an organizational physics problem, not a computer science problem. The disconnect is further exacerbated by the differing timelines of developers versus executives. Developers and researchers live in a world of root access and rapid iteration, often blinded to the molasses-like speed of corporate change management. A CFO, conversely, thinks in fiscal quarters and compliance audits. They require proven ROI and standardized best practices before signing off on widespread automation. Currently, there are no industry standards for deploying autonomous agents, which halts most conversations at the boardroom door. Therefore, the “stalling” progress is an illusion caused by looking at the wrong metrics. If you look at model cards and benchmarks, progress is linear and fast. If you look at widespread economic integration, the curve is flat. This S-curve of adoption is always significantly behind the S-curve of capability. We are in the flat part of the adoption curve, characterized by high hype, high friction, and frantic infrastructure building behind the scenes. The next three to five years will likely be dominated not by flashy new model capabilities, but by the “boring” work of integration. We will see the rise of startups and consultancies dedicated solely to “AI Governance,” “Agent Identity Management,” and “Context Orchestration.” These are the boring rails that will eventually allow the high-speed train of AI to actually run. The output gap is a temporary, necessary phase. It is the silence before the orchestra starts playing. The potential energy is building up in the form of capability, but it cannot convert into kinetic economic energy until the friction is reduced. The “stalled” revolution is simply a revolution that is currently under construction. We are not witnessing the ceiling of AI intelligence; we are witnessing the floor of AI adoption. The technology has done its part; now, the organizations must do theirs. The future isn’t late—it’s just waiting for legal to sign off.
26:05

Dwarkesh is WRONG about the "Output Gap" The narrative around Artificial Intelligence has shifted perceptibly in late 2025. After years of exponential hype, a sense of disillusionment has begun to settle over the industry. Commentators and analysts, most notably podcaster and writer Dwarkesh Patel, have recently highlighted what is being called the “Output Gap.” This is the uncomfortable discrepancy between our models’ skyrocketing performance on benchmarks and the relatively stagnant growth in macroeconomic productivity. We have reached “superhuman” capability on tests, yet global GDP hasn’t skyrocketed, and the promised revolution feels curiously delayed. This frustration stems from a fundamental misunderstanding of where we are in the technology cycle. The industry is currently fixated on “Day 0” capabilities—the raw intelligence of the models, the scaling laws, and the saturation of academic benchmarks. However, the bottleneck has shifted. We are no longer limited by the intelligence of the model, but by the inertia of the enterprise. The “Output Gap” is not a failure of technology; it is a lag in organizational digestion. We have invented a powerful jet engine, but we are essentially frustrated that it hasn’t revolutionized travel before we’ve even built the airframe to mount it on. The primary fallacy driving this disappointment is the expectation of the “drop-in remote worker.” Many observers equate Artificial General Intelligence (AGI) with a digital human that can be onboarded, culturally assimilated, and left to run autonomously with minimal supervision. Because current agents cannot seamlessly replace a human employee in this one-to-one fashion, the conclusion is often that the technology has stalled. This view misses the forest for the trees. Disruptive technologies rarely act as direct replacements; instead, they require a complete restructuring of how work is done. In reality, the barrier to adoption is what IT professionals call “Day 2 Operations.” Day 0 is the exciting launch; Day 2 is the boring, messy reality of governance, security, and maintenance. For an enterprise to deploy an autonomous agent, it isn’t enough for the model to be smart. The organization must solve for Role-Based Access Control (RBAC), SOC 2 compliance, liability frameworks, and data sovereignty. Right now, most organizations lack the infrastructure to handle “non-human” identities that have access to sensitive corporate data. Consider the security implications. A human employee has physical limitations and a single identity. An AI agent is an “always-on” entity that, if given improper permissions, could theoretically read every email in a company server to “optimize workflow.” Security teams (CISOs) are rightly terrified of this prospect. Until we develop granular access controls specifically designed for agents—effectively an “RBAC for AI”—the widespread deployment of autonomous agents will remain blocked by the “Departments of No”: Legal, HR, and Security. This operational friction explains why we are seeing a massive divergence between individual and enterprise adoption. Individually, adoption is rampant; we are in the “Shadow IT” era of AI, where employees secretly use tools like ChatGPT to boost their personal productivity. However, at the organizational level, adoption is glacial because the institution’s primary mandate is risk management, not speed. The C-suite is asking questions about ROI, liability, and data leakage that the current software ecosystem cannot yet answer satisfactorily. History offers a comforting precedent for this timeline. We are currently effectively in the “2002 era” of virtualization. In the early 2000s, virtualization technology (like VMware) was technically viable, but it took nearly a decade to become the default enterprise standard. It required years of maturing management software, security protocols, and cultural shifts before “The Cloud” became a reality. AI is undergoing the same digestion period. The technology is ready, but the enterprise “rails” required to run it are still being laid. This leads us to the “Mechanical Horse” fallacy. When the automobile was invented, people didn’t need a mechanical horse that walked on four legs; they needed a car. But the car required asphalt roads, not dirt paths. Similarly, we are currently trying to force AI into human-shaped workflows (”jobs”) rather than rebuilding the workflows to suit the AI. We are waiting for the mechanical horse, failing to realize that the true disruption comes from unbundling the work entirely. A “job” is essentially a bundle of tasks, context, and responsibilities aggregated for a single human. AI does not replace jobs; it unbundles tasks. The transition we are navigating involves breaking down these bundles and determining which specific value streams can be automated by agents. This requires task decomposition and new management frameworks—a “Council of AIs” approach—rather than a single omnipotent bot. This restructuring is an organizational physics problem, not a computer science problem. The disconnect is further exacerbated by the differing timelines of developers versus executives. Developers and researchers live in a world of root access and rapid iteration, often blinded to the molasses-like speed of corporate change management. A CFO, conversely, thinks in fiscal quarters and compliance audits. They require proven ROI and standardized best practices before signing off on widespread automation. Currently, there are no industry standards for deploying autonomous agents, which halts most conversations at the boardroom door. Therefore, the “stalling” progress is an illusion caused by looking at the wrong metrics. If you look at model cards and benchmarks, progress is linear and fast. If you look at widespread economic integration, the curve is flat. This S-curve of adoption is always significantly behind the S-curve of capability. We are in the flat part of the adoption curve, characterized by high hype, high friction, and frantic infrastructure building behind the scenes. The next three to five years will likely be dominated not by flashy new model capabilities, but by the “boring” work of integration. We will see the rise of startups and consultancies dedicated solely to “AI Governance,” “Agent Identity Management,” and “Context Orchestration.” These are the boring rails that will eventually allow the high-speed train of AI to actually run. The output gap is a temporary, necessary phase. It is the silence before the orchestra starts playing. The potential energy is building up in the form of capability, but it cannot convert into kinetic economic energy until the friction is reduced. The “stalled” revolution is simply a revolution that is currently under construction. We are not witnessing the ceiling of AI intelligence; we are witnessing the floor of AI adoption. The technology has done its part; now, the organizations must do theirs. The future isn’t late—it’s just waiting for legal to sign off.

David Shapiro (L/0)

12,656 views • 8 months 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