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TPUs Via Cloud Next, Intel Earnings, Foundry Scarcity Ben Bajarin and Jay Goldberg 顾忠南 discuss: 00:00 - Ben and Jay introduce an action-packed episode covering a massive week of semiconductor announcements. 00:10 - Ben recaps his experience at Google Next, highlighting the launch of the new TPU v5p and... v5i accelerators. 02:35 - The hosts discuss the physical design of the new inference chips and how they differ from training hardware. 03:04 - A deep dive into memory architecture explores the use of HBM and the "Boardfly" networking innovation. 04:15 - Jay notes reports of Google working on SRAM-based inference chips to solve the HBM supply bottleneck. 07:22 - Google's leadership draws a historical parallel between today's AI training costs and their early days of indexing the web. 11:11 - The duo critiques the enterprise focus of the Google Next keynote and the current productivity gap in Gemini. 15:24 - Intel's recent earnings report shows a significant beat on revenue and guidance driven by a "CPU resurgence". 18:31 - Ben and Jay debate the strategic advantage of Intel owning its own fabs during a global capacity shortage. 25:51 - The conversation shifts to Intel's manufacturing roadmap and the potential for Tesla to become a 14A customer. 33:23 - An analysis of Intel's new financial reporting structure reveals the massive internal scale of their foundry business. 40:27 - Recent drama at the TSMC Symposium is discussed, specifically regarding the high cost of ASML's High-NA EUV tools. 44:23 - Growing demand for AI and memory is driving Wafer Fabrication Equipment (WFE) forecasts to record highs. 47:06 - Ben and Jay conclude by debating the long-term durability of the current semiconductor bull cycleshow more

The Circuit

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12,020 次观看 • 3 个月前 •via X (Twitter)

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More good News for Intel, AI Model Trends, Future of Datacenter With Ben Bajarin and Jay Goldberg 顾忠南 Chapters (00:00) Intel's New Partnership with Tesla (03:02) Market Reactions and Implications for Intel (05:53) Google's Collaboration with Intel (08:56) The Semiconductor Market Dynamics (12:06) Valuation Discrepancies in the Semiconductor Sector (14:59) Deep Dive into Semiconductor Supply Chains (17:56) Future of Data Centers and Energy Efficiency (22:38) The Evolution of Data Center Design (31:24) AI's Impact on Software and Productivity (39:10) Token Usage and Efficiency in AI (42:35) Cost Dynamics in the Semiconductor Industry

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Aidan Gomez (Aidan Gomez) is a computer scientist, co-author of the seminal paper ‘Attention Is All You Need,’ and the CEO of Cohere. In this episode, we discussed his upbringing in the cabin his grandfather built in Codrington, a small town North of Brighton, and the values that were instilled in him through his family. We explored his path from Codrington to his undergraduate studies at the University of Toronto, emailing Geoffrey Hinton, and joining Google Brain where he co-wrote the paper on transformers. We discussed how he met his co-founders Ivan Zhang and Nick Frosst, and his insights on what it means to build a meaningful, successful company in Canada. Aidan shares his conviction about what is at stake — for Canada and for the world at large. This is a conversation about family, values, and what it means to live with conviction. The Other Stuff is hosted by internetVin — filmmaker, entrepreneur, and possibly the most curious man on Earth. Produced by New. The Other Stuff #29 — Aidan Gomez: Empathy and Conviction — Timestamps 00:00:00 Intro 00:03:10 The Malleability of Toronto 00:11:06 Growing Up in Codrington 00:13:50 The Story of Aidan Gomez’s Family 00:27:23 Introduction to the Internet 00:30:39 Values and Work Ethic 00:35:46 University of Toronto’s AI Scene 00:40:48 Emailing Geoffrey Hinton 00:42:16 Google Brain 00:45:15 Dropout: A Simple Way to Prevent Neural Networks from Overfitting 00:49:48 The Beauty of Research 00:54:24 One Model to Rule Them All 00:59:54 Meeting Ivan Zhang and Nick Frosst 01:04:00 The Birth of Cohere 01:06:56 Twitter Influencers and Alex Friedland 01:11:48 Being the CEO 01:12:51 Building for Canada 01:15:01 Three Fundamental Ingredients of Building a Company 01:21:39 Working with the Canadian Government 01:24:39 Reflexivity in Canada 01:36:22 What Is Evil? 01:42:40 The Role of AI in the World

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45,297 次观看 • 8 个月前

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66,172 次观看 • 4 个月前

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️

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Proof of Coverage | Tech’s Creative Agency

98,908 次观看 • 10 个月前

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.

Karl Mehta

25,716 次观看 • 1 个月前

The recent semiconductor selloff has spread worldwide because the supply chain spans U.S. chip design and cloud spending, Taiwanese manufacturing, Korean memory, Japanese materials, European equipment and Southeast Asian packaging. That makes $MSFT earnings today massive as its June fiscal year-end offers the first major look at spending plans for the first 6 months of 2027 across accelerators, CPUs, memory, networking, optics, advanced packaging, power and equipment. If Microsoft confirms AI demand remains above capacity and capex is still growing 30% to 40% then the selloff will look more like a financing and risk-off reset than evidence the semiconductor cycle is breaking.

The recent semiconductor selloff has spread worldwide because the supply chain spans U.S. chip design and cloud spending, Taiwanese manufacturing, Korean memory, Japanese materials, European equipment and Southeast Asian packaging. That makes $MSFT earnings today massive as its June fiscal year-end offers the first major look at spending plans for the first 6 months of 2027 across accelerators, CPUs, memory, networking, optics, advanced packaging, power and equipment. If Microsoft confirms AI demand remains above capacity and capex is still growing 30% to 40% then the selloff will look more like a financing and risk-off reset than evidence the semiconductor cycle is breaking.

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78,929 次观看 • 22 天前

Join my conversation with Elon Musk on AGI timelines, energy, robots, and why abundance is the most likely outcome for humanity's future, alongside my Moonshot Mate NOTDAVID! (00:00) - Navigating the Future of AI and Robotics (04:54) - The Promise of Abundance and Optimism (10:02) - Energy: The Key to a Sustainable Future (15:00) - The Role of Education in a Changing World (41:07) - Health, Longevity, and the Future of Humanity (50:51) - AI's Impact on Labor and Employment (55:05) - Universal High Income: A New Economic Paradigm (57:58) - Navigating the Singularity and AI's Acceleration (01:02:30) - The Role of AI in Healthcare and Surgery (01:08:22) - Ethics and AI: Programming Values into Machines (01:14:18) - The Future of Space Exploration and AI's Role (01:33:30) - The Chip Shortage Crisis (01:42:46) - Simulation Theory and Consciousness (01:48:18) - The Search for Extraterrestrial Life (01:58:28) - The Future of Robotics and AI Integration

Join my conversation with Elon Musk on AGI timelines, energy, robots, and why abundance is the most likely outcome for humanity's future, alongside my Moonshot Mate NOTDAVID! (00:00) - Navigating the Future of AI and Robotics (04:54) - The Promise of Abundance and Optimism (10:02) - Energy: The Key to a Sustainable Future (15:00) - The Role of Education in a Changing World (41:07) - Health, Longevity, and the Future of Humanity (50:51) - AI's Impact on Labor and Employment (55:05) - Universal High Income: A New Economic Paradigm (57:58) - Navigating the Singularity and AI's Acceleration (01:02:30) - The Role of AI in Healthcare and Surgery (01:08:22) - Ethics and AI: Programming Values into Machines (01:14:18) - The Future of Space Exploration and AI's Role (01:33:30) - The Chip Shortage Crisis (01:42:46) - Simulation Theory and Consciousness (01:48:18) - The Search for Extraterrestrial Life (01:58:28) - The Future of Robotics and AI Integration

Peter H. Diamandis, MD

6,233,712 次观看 • 7 个月前

I had a chance to sit down with Demis Hassabis to discuss his goal of solving intelligence and using it to help cure all disease. But what I found most compelling wasn't the scale of the mission, but Demis Hassabis himself. His level of focus is extraordinary. We talked about the personal cost of pursuing a mission this ambitious, and the tension between the two sides of his work: the CEO of a commercial entity and the scientist driven by a humanitarian vision. It was an absolutely fascinating conversation, and a deep honor to speak with one of the most important minds of our time. Thank you to the Google DeepMind team for having me. (0:00) Curing Every Disease in a Decade (1:04) Gemini for Science (2:01) Changing Science Forever with AlphaFold (4:04) Can Humans Trust AI In The Medical Field? (6:02) Generative AI vs. Practical AI (7:03) The Tension Between CEO & Scientist (8:28) AGI by 2030 (10:07) The Chess Prodigy Turned CEO (11:20) Ender's Game & What This Mission Has Cost

I had a chance to sit down with Demis Hassabis to discuss his goal of solving intelligence and using it to help cure all disease. But what I found most compelling wasn't the scale of the mission, but Demis Hassabis himself. His level of focus is extraordinary. We talked about the personal cost of pursuing a mission this ambitious, and the tension between the two sides of his work: the CEO of a commercial entity and the scientist driven by a humanitarian vision. It was an absolutely fascinating conversation, and a deep honor to speak with one of the most important minds of our time. Thank you to the Google DeepMind team for having me. (0:00) Curing Every Disease in a Decade (1:04) Gemini for Science (2:01) Changing Science Forever with AlphaFold (4:04) Can Humans Trust AI In The Medical Field? (6:02) Generative AI vs. Practical AI (7:03) The Tension Between CEO & Scientist (8:28) AGI by 2030 (10:07) The Chess Prodigy Turned CEO (11:20) Ender's Game & What This Mission Has Cost

Roberto Nickson

51,426 次观看 • 2 个月前

Today’s pod is with CZ 🔶 BNB founder and former CEO of Binance to discuss: - What Binance got right during its launch and how it climbed to the top (and stayed there) - Why CZ was surprised by stablecoin’s sticking power - Where AI is going next both in and outside of crypto TIMESTAMPS: 00:00 - AI, Crypto, and the Future of Payments 00:33 - Building Binance in 2017 and Moving Faster 03:44 - How Binance Reached the Top 05:48 - Building a Community-Driven Platform 08:27 - Creating Products People Return To 10:31 - Crypto as Foundational Technology 11:33 - Where AI and Crypto Intersect 14:26 - Using AI as a Productivity Tool 18:37 - The Next Opportunities in Crypto 21:21 - Building at the Intersection of Skills and Value 21:53 - The Future of Traditional and On-Chain Finance Watch below or on X:

Today’s pod is with CZ 🔶 BNB founder and former CEO of Binance to discuss: - What Binance got right during its launch and how it climbed to the top (and stayed there) - Why CZ was surprised by stablecoin’s sticking power - Where AI is going next both in and outside of crypto TIMESTAMPS: 00:00 - AI, Crypto, and the Future of Payments 00:33 - Building Binance in 2017 and Moving Faster 03:44 - How Binance Reached the Top 05:48 - Building a Community-Driven Platform 08:27 - Creating Products People Return To 10:31 - Crypto as Foundational Technology 11:33 - Where AI and Crypto Intersect 14:26 - Using AI as a Productivity Tool 18:37 - The Next Opportunities in Crypto 21:21 - Building at the Intersection of Skills and Value 21:53 - The Future of Traditional and On-Chain Finance Watch below or on X:

Talking Tokens Podcast

412,163 次观看 • 1 个月前

SITUATION EXPLAINED: Cerebras raised $5.55 billion in their IPO and closing their first day of trading valued at $66 billion, making it the biggest US tech IPO since Snowflake in 2020. Cerebras makes Wafer-Scale Engine chips built for AI inference. We asked Sarah Fong the main difference between wafer-scale chips and traditional GPUs: - GPUs are great at parallel work (graphics, training) - AI inference is sequential, AKA one token at a time This causes the "memory wall" problem: - Every GPU core needs model weights, KV cache, and activations to do its math - On a GPU, that data lives in off-chip memory (HBM) - Cores constantly load and offload from off-chip memory, which is a huge bottleneck; hardware accounts for ~70% of inference latency Cerebras' chips: -Dinner-plate sized (vs. GPUs which are palm-sized) with tens of thousands of cores -Memory sits directly on top of the cores as distributed SRAM -Weights and KV cache can be accessed at on-chip speeds in the PB/s range, compared with off-chip speeds in the TB/s range achieved by GPUs with HBM.

SITUATION EXPLAINED: Cerebras raised $5.55 billion in their IPO and closing their first day of trading valued at $66 billion, making it the biggest US tech IPO since Snowflake in 2020. Cerebras makes Wafer-Scale Engine chips built for AI inference. We asked Sarah Fong the main difference between wafer-scale chips and traditional GPUs: - GPUs are great at parallel work (graphics, training) - AI inference is sequential, AKA one token at a time This causes the "memory wall" problem: - Every GPU core needs model weights, KV cache, and activations to do its math - On a GPU, that data lives in off-chip memory (HBM) - Cores constantly load and offload from off-chip memory, which is a huge bottleneck; hardware accounts for ~70% of inference latency Cerebras' chips: -Dinner-plate sized (vs. GPUs which are palm-sized) with tens of thousands of cores -Memory sits directly on top of the cores as distributed SRAM -Weights and KV cache can be accessed at on-chip speeds in the PB/s range, compared with off-chip speeds in the TB/s range achieved by GPUs with HBM.

MTS

44,057 次观看 • 3 个月前