SemiAnalysis Weekly

Jordan Nanos, Doug O'Laughlin

Everything semiconductors and AI Covering the spectrum

  1. 2 dgn geleden

    Ep. 015 - DG Matrix Explains 800V DC vs Legacy AC Distribution (Datacenter, Energy) | Jordan Nanos, Jeremie Eliahou Ontiveros, Nicolas Bontigui, Haroon Inam

    NVIDIA's next architecture will demand 800V DC and datacenters can't wait to build the infrastructure. Haroon Inam, CEO and Co-Founder of DG Matrix, joins Jordan Nanos (@JordanNanos), Jeremie Eliahou Ontiveros (@JeremieEO), and Nicolas Bontigui to explain why megawatt GPU racks make 800V DC a necessity, not an option. Haroon details his journey from uncool power electronics to enabling superhuman intelligence infrastructure. Full Article Link: https://newsletter.semianalysis.com/p/inside-the-800vdc-revolution-part Chapters: 00:00 Haroon Inam's Background and Power Electronics Journey 00:29 Introduction to 800V DC Architecture and Its Significance 02:19 Why 800V? Economics, Semiconductor Ratings, and EV Influence 04:47 Physics and Economics Driving the 800V Revolution 08:27 DG Matrix's Multi-Port SST and Its Value Proposition 11:33 Design Challenges and Innovations in Multi-Port SSTs 13:53 Current State and Adoption of 800V DC in Data Centers 17:51 Future Data Center Architectures and Power Density Trends 22:08 Adoption Curve and Market Penetration of 800V DC 26:34 Risks, Challenges, and Future Proofing of Data Centers 30:36 Cybersecurity and Power System Resilience 38:04 The Broader Impact of Power Innovations on Society

    48 min.

  2. 4 jun

    Ep. 014 - Finding Miscompiles For Fun, Not Profit (AI Infrastructure) | Justin Lebar & Jordan Nanos

    Justin Lebar (jlebar.com) recently spent $10,000 in an afternoon, uncovering critical miscompiles across NVIDIA's PTXAS, LLVM's AMD GPU, and X86 backends. He joins Jordan Nanos (@JordanNanos) to detail his methodology, which combined traditional fuzzing techniques with novel LLM-assisted bug finding. Their discussion highlights the unique challenges of detecting flaws in less-tested ML compilers compared to mature CPU environments.Lebar shares specific high-severity X86 findings, including an atomic operation bug that splits into two non-atomic operations. They explore the comparative efficacy of fuzzing versus LLM agents in identifying these elusive errors. This episode offers critical insights into compiler security and the burgeoning role of AI in automating rigorous code verification for AI infrastructure.FULL ARTICLE 00:00 Introduction and Content Overview00:25 Justin Lebar's Background and Recent Project00:59 Fuzzing Techniques for Compiler Bugs01:56 Motivation Behind the Project02:48 Challenges in Bug Detection in GPU and ML Compilers04:13 Bug Severity and Findings in AMD and x8605:38 Using LLMs to Read and Find Bugs in Code07:56 Impact of New Models and UltraCode Mode12:18 Estimating Time and Effort Without AI Assistance14:22 Limitations of Manual Code Review for Bugs15:03 Optimism About AI in Software Development16:17 Next Steps and Future Projects18:11 Key Takeaways for Developers and Researchers21:48 Call for Community Engagement and Scientific Approach

    23 min.

  3. 1 jun

    Ep. 013 - AWS Margins Jump 10% While Azure and GCP Flatline (Tokenomics) | Jordan Nanos, Jeremie Eliahou Ontiveros, Joey Brookhart, Crystal Huang

    AWS operating margins jumped 10 percentage points while Microsoft Azure and Google Cloud stayed flat. The driver: Anthropic's Claude usage routing through Bedrock, Amazon's token-as-a-service platform. Jordan Nanos (@JordanNanos), Jeremie Eliahou Ontiveros (@JeremieEO), Joey Brookhart (@SaasquatchC), and Crystal Huang (@Egg1459) break down why stabilized token margins are fundamentally richer than GPU-as-a-service for hyperscalers. The crew analyzes Anthropic's recent $65B Series H raise, Claude Opus 4.8 release, and SpaceX partnership against the backdrop of 300+ neo clouds fragmenting the traditional cloud moat.The team forecasts how AWS's workload mix advantage creates sustainable returns while competitors struggle with asset-heavy GPU service models. They examine the $22.7T TAM question, earnings-before-training dynamics, and whether the 2026 AI infrastructure beat belongs to silicon vendors or platform integrators. Subscribe for weekly deep dives into semiconductor and AI infrastructure economics.00:00 Intro: Episode 13 and the AWS margins article00:56 What is Bedrock? The three hyperscaler buckets02:33 AWS margins rising while peers lag03:33 Cloud moats collapsing and the neo cloud explosion06:32 Why stabilized token-as-a-service margins are so rich09:54 Amazon's workload mix advantage12:41 Forecasting Anthropic and the 4.8 release16:33 The SpaceX deal and the $65B Series H raise19:30 Bullish or bearish? Demand becoming supply28:55 The $22.7T TAM and does the race even matter31:59 Earnings before training and open-ended TAM36:27 The 2026 beat is basically one company40:22 Who wins long term: silicon, partnerships, integration

    44 min.

  4. 15 mei

    Ep. 012 - Cerebras IPO: 1,100 Tokens Per Second vs GPU's 500 (AI Cloud TCO) | Jordan Nanos, Howie , Myron Xie

    Jordan Nanos (@JordanNanos), Howie, and Myron Xie break down the economics of Cerebras's IPO on the eve of their public debut, examining their OpenAI and Amazon deals that have shifted the company away from Middle Eastern investor concentration toward frontier AI labs willing to pay exponentially more for speed.The discussion covers Cerebras's radical stitching innovation across a full wafer, creating compute density equivalent to an entire NVL72 rack without off-chip data movement. The hosts analyze whether businesses will accept these premium economics as fast tokens become the new standard for interactive AI applications.Subscribe for weekly deep dives into semiconductor economics and AI infrastructure developments. (00:00) Cerebras IPO Preview (18:58) Need for Speed Fast Tokens (21:19) Wafer Scale Engine Architecture (25:31) Radical Stitching Innovation (31:55) Power Delivery and Cooling (34:31) Bandwidth and IO Limitations (37:12) Scaling Beyond Wafer Size (40:05) Manufacturing and Assembly Bottlenecks (42:54) Data Center Service Model

    45 min.

  5. 6 mei

    Ep. 011 - GPT 5.5 vs Claude 4.7: OpenAI's Comeback From the Brink (Tokenomics) | Jordan Nanos, Dylan Patel, Doug O'Laughlin, Max Kan

    OpenAI was in serious trouble at the beginning of this year. Anthropic's Claude Opus 4.5 release had triggered a wave of developers to start using Claude Code, pushing Anthropic's revenue past OpenAI's on a like-for-like basis by April. OpenAI's GPT 5.4 response was such an embarrassment they didn't even compare it to Claude in their model release card. Then came GPT 5.5 - finally back on the frontier, but is it enough to reclaim the crown? Jordan Nanos (@JordanNanos), Dylan Patel (@Dylan522p), Doug O'Laughlin (@FabricatedKnowledge), and Max Kan (@maxkan_) break down the latest AI model wars, from Claude 4.7's coding dominance to DeepSeek's long-delayed v4 release and what it reveals about China's AI capabilities. They analyze token efficiency, benchmark gaming, and why fast mode might be fake news. Subscribe for weekly deep dives into the semiconductor and AI infrastructure powering the future. The Coding Assistant Breakdown AI Value Capture Timestamps: 00:00 OpenAI's Comeback and the Latest AI Model Wars 04:05 The High Cost of AI Models and Fast Mode Effectiveness 08:16 When AI Tokens Become Too Expensive for Tasks 13:11 Why AI Model Quality Degrades and Benchmarks Fail 18:42 Deep Dive into Claude 4.7 Features and Tokenizer Changes 25:29 DeepSeek's Release and China's AI Compute Constraints 28:20 The Future of Context Windows and Agent Orchestration 30:47 The Great Debate: CLI vs. App for AI Interaction 36:33 Debunking AI Fake News and Context Window Limitations 40:51 The AI Race: China, Meta, and the Neo Cloud Vision 43:46 Final Thoughts and Listener Feedback Request

    45 min.

  6. 1 mei

    Ep. 010 - How Much Do GPUs Really Cost, and Where Does the Value Go? (AI Cloud TCO) | Jordan Nanos, Dan Nishball, Kang Wen Cheang, Zane Fong

    This episode features Jordan Nanos (@JordanNanos) and Daniel Nishball (@dnishball) breaking down the economics of GPU clusters through real-world data and experience. Joined with Kang Wen Cheang and Zane Fong, the team discussed moving beyond theoretical TCO models as they examine how reliability differences between top-tier and lower-tier providers create significant cost disparities that aren't captured in simple per-GPU pricing. The discussion introduces practical frameworks for measuring goodput and understanding how system failures cascade through entire training jobs.Nanos walks through the mechanics of fault-tolerant frameworks including AWS's Checkpointless Training and explains why a single GPU failure can halt progress across hundreds of nodes. The conversation reveals how hyperscalers and NeoClouds price their services and why paying premium rates for reliable infrastructure often delivers better value than chasing the lowest per-hour costs. Subscribe to SemiAnalysis for in-depth analysis of AI hardware economics and infrastructure trends that impact the entire semiconductor ecosystem.

    47 min.

  7. 19 apr

    Ep. 009 - Using Open Source Data To Drive Investment Decisions (ChipBook) | Chaim Eisenberg, Simi Sherman, Jordan Nanos

    This week the team from ChipBook (formerly Chips & Wafers) joins. Jordan Nanos talks with Chaim Eisenberg and Simi Sherman as they explore how they build the ChipBook with open source data, and how that drives investment decisions. This episode dives deep into the ChipBook itself, revealing how granular, historical data collection provides insights into supply chain dynamics, memory markets, wafer fabrication equipment trends and more. The guests also share compelling examples of how their data-driven approach has generated some viral social media recently.for more: SemiAnalysis.com/chipbook00:00 The Chipbook: Understanding Open Source Data07:56 Granularity in Data: The Key to Investment Insights09:11 Understanding the Semiconductor Supply Chain10:52 Memory Market Insights and Trends14:23 Tracking WFE and Its Impact on Production15:20 The Importance of Early Signals in Investment16:51 Geopolitical Implications on Semiconductor Supply20:24 The Impact of Tariffs and Regulations22:48 Granular Tracking for Investment Decisions27:07 Data-Driven Insights and Investment Strategies29:13 The Structure of the Chipbook33:02 Collaboration and Integration at Semi Analysis37:02 Geopolitical Analysis and Its Impact on TSMC43:48 Helium Supply Chain and Its Importance

    53 min.

  8. 10 apr

    Ep. 008 Claude Code Psychosis: How SemiAnalysis Is Token Mogging Meta | Dan Nishball, Sam Harshe, Jordan Nanos

    Jordan Nanos (@jordannanos) Daniel Nishball (@dnishball) and Sam Harshe (@sharshe02) break down how SemiAnalysis is deploying Claude Code agents across its Singapore office at a scale that outpaces Meta on a per-employee basis. They cover the practical workflow changes, the trust and reliability questions that come with AI-generated analysis, and what it actually takes to build an agent swarm that does useful work. The conversation also gets into cybersecurity risks and where AI model development is headed next.00:00 - Introduction and Team Dynamics09:10 - The Evolution of Agent Utilization14:49 - Conference Insights and Research Efficiency15:58 - AI's Role in Learning and Analysis21:13 - Trust and Reliability in AI Outputs27:04 - Market Impact and Adoption of AI Tools32:14 - Cybersecurity and AI: Opportunities and Challenges39:32 - Future of AI Models and User Experience

    35 min.
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Everything semiconductors and AI Covering the spectrum

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    Jordan Nanos, Doug O'Laughlin
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    © Jordan Nanos, Doug O'Laughlin
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