SemiAnalysis Weekly

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 BreakdownAI Value CaptureTimestamps:00:00 OpenAI's Comeback and the Latest AI Model Wars04:05 The High Cost of AI Models and Fast Mode Effectiveness08:16 When AI Tokens Become Too Expensive for Tasks13:11 Why AI Model Quality Degrades and Benchmarks Fail18:42 Deep Dive into Claude 4.7 Features and Tokenizer Changes25:29 DeepSeek's Release and China's AI Compute Constraints28:20 The Future of Context Windows and Agent Orchestration30:47 The Great Debate: CLI vs. App for AI Interaction36:33 Debunking AI Fake News and Context Window Limitations40:51 The AI Race: China, Meta, and the Neo Cloud Vision43:46 Final Thoughts and Listener Feedback Request

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.

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

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

The Core Research team is on SemiAnalysis Weekly this week. Jordan Nanos, Nick Doyle, Nigel Chiang, and Konrad Wang walk through the biggest bottlenecks in AI infrastructure: TSMC capacity constraints, PCB and substrate shortages, memory cycle dynamics, modular data center construction, and behind-the-meter power. The team also gets into how Claude Code is transforming their day-to-day research, what enterprise AI adoption actually looks like today, and the signals they're watching for when the cycle turns.

This week, Sravan Kundojjala (@SKundojjala) and Ivan Chiam ( from our team join Jordan (@JordanNanos) to break down the AI silicon shortage — and why its ripple effects are hitting everything from GPU pricing to your next smartphone.We cover what's driving the crisis, how TSMC is allocating scarce capacity across its biggest customers, and why memory constraints could cut consumer electronics production by 10–15%. We dig into TSMC's $70B+ capex plans, the structural dynamics reshaping the memory market, and near-term node migration strategies that could offer some relief. Then we shift to the GPU rental market, tracking real pricing trends and what they signal about supply and demand heading into the back half of the year.In the second half, we unpack Nvidia's co-packaged optics (CPO) roadmap — one of the most significant infrastructure announcements to come out of recent industry events. We cover highlights from the OFC conference, explain why optical interconnects matter for next-gen AI clusters, and break down the dueling MSA standards battle playing out across the optical components industry.Whether you're an investor, engineer, or just trying to understand why AI hardware is so hard to get right now — this one's for you.Jordan Nanos (Chapters00:00 Introduction and Episode Overview00:30 AI Silicon Shortage: Causes and Demand Growth03:25 TSMC's Capacity Constraints and Customer Allocation06:24 Impact of Memory Shortages on Consumer Electronics10:16 Memory Market Dynamics and Structural Trends13:25 Near-term Solutions and Node Migration Strategies15:31 Modeling the Memory Shortage and Industry Outlook17:16 Signs of Relief and Demand Trends19:35 Capex and Industry Investment Outlook23:25 GPU Rental Market and Pricing Trends26:58 Nvidia's CPO Roadmap and Industry Implications39:26 OFC Conference Highlights and Optical Interconnects40:24 Understanding Co-Package Optics (CPO) and Its Significance46:06 Industry Significance of Nvidia's Announcements and Market Outlook50:04 Dueling MSAs and Industry Standardization in Optical Components56:31 Summary and Final Thoughts on Industry Trends

This episode explores the impact of AI on memory costs, market dynamics, and economic measurement. Our experts discuss how AI is transforming industries, the challenges of measuring economic value, and the future of AI adoption across sectors. This week Jordan hosts Ray Wang, Malcolm Splitter, Joey Brookhart from SemiAnalysis.Chapters00:00 Introduction and Guest Credibility03:44 Memory Constraints and Market Impact07:14 Elasticity of Supply and Economic Analogies10:00 AI Adoption in Personal and Enterprise Life14:57 Global Distribution of AI Usage and Tokens20:00 Consumer vs Enterprise AI Use Cases24:56 Future Market Growth and Adoption Scenarios29:48 AI's Impact on GDP and Economic Measurement40:12 AI and the Future of Work and Productivity50:02 Global Workforce and AI's Economic Effects01:00:03 Building AI-Driven Dashboards and Tools01:09:59 Reevaluating GDP and Economic Value of AI01:19:56 Closing Remarks and Future Outlook

Join Jordan Nanos, Doug O’Laughlin, and special guest Jeremie Eliahou Ontiveros for an in-depth analysis of the intersection between AI infrastructure and energy markets. Jeremie, Head of Datacenter & Energy Infrastructure Research, shares expert insights on how AI data centers are reshaping electricity pricing, market dynamics, and the regulatory challenges facing the grid today.00:00 AI Data Centers and Electricity Prices02:15 Market Dynamics: PJM vs. ERCOT04:56 Supply and Demand Challenges08:02 Thermal Accreditation and Market Reforms11:05 Future of Energy Supply and Coal Retirements17:13 The Future of Coal Power Plants18:27 Balancing Energy Prices and Consumer Expectations19:29 Customized Tariffs and Their Impact20:28 Investment Commitments and Grid Reliability22:30 Negotiation Timelines for Data Centers24:32 Financing Mismatches in Energy Projects25:10 The AI Boom and Its Impact on Energy27:13 Anthropic vs. OpenAI: The Revenue Race29:15 The Role of Government in AI Adoption32:00 Demand Drivers in AI and Software Development33:33 The Shift in Consumer Perception of AI35:53 The Future of Coding and AI Integration39:55 The Evolution of Software and AI Tools43:53 The Canadian Tech Scene and AI Growth

Article (latest): https://newsletter.semianalysis.com/p... 00:00 Introduction to Vera Rubin and Extreme Co-Design 02:30 Innovations in GPU Architecture: From Blackwell to Rubin 05:38 Memory Bandwidth and HBM4: A New Era 08:26 NVLink 6 and Interconnect Enhancements 11:38 Cable-less Design: Revolutionizing System Assembly 14:29 Thermal Management Innovations in Rubin 17:30 Power Management and Performance Expectations 33:36 Innovations in Packaging and Heat Management 38:15 Chiller-less Design and Data Center Infrastructure 40:47 Power Delivery Innovations in Rubin 46:16 Memory Solutions and Supply Chain Management 54:57 Deployment Timeline and Future Expectations

InferenceX, formerly InferenceMAX: https://inferencex.com/ Article (latest): https://newsletter.semianalysis.com/p/inferencex-v2-nvidia-blackwell-vs GitHub https://github.com/SemiAnalysisAI/InferenceX Article (original) https://newsletter.semianalysis.com/p/inferencemax-open-source-inference 00:00 Introduction to InferenceX 02:52 Evolution from InferenceMAX to InferenceX 06:06 Benchmarking and Performance Insights 08:43 The Scale of Benchmarking Work 11:39 Collaboration with AMD and Nvidia 14:52 The Evolution of Inference Benchmarking 17:34 Optimizations and Their Impact 20:47 Challenges in Composability 23:51 Multi-Token Prediction Explained 26:52 Cost Implications of Optimizations 31:06 Understanding Inference Workloads and Benchmarks 33:44 Future Plans for Inference Optimization 37:16 Roadmap for New Models and Data Sets 39:03 Challenges in Benchmarking Multi-Turn and Multi-Modal Data 42:44 Experiences with AI Models and Their Limitations 48:43 Skepticism About Future AI Improvements

Claude Code Rising: https://semianalysis.com/institutional/claude-code-adoption-note/ and https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point CPUs are back: https://newsletter.semianalysis.com/p/cpus-are-back-the-datacenter-cpu Memory Mania: https://newsletter.semianalysis.com/p/memory-mania-how-a-once-in-four-decades Claude Fast: https://x.com/SemiAnalysis_/status/2020922445989822709?s=20 Agent Swarms: https://x.com/SemiAnalysis_/status/2021283054019330194?s=20 Taiwan: https://x.com/SemiAnalysis_/status/2021222800707538980?s=20 Seedance2: https://x.com/kirkinator_sol/status/2012588116536631778?s=20 , https://docs.google.com/document/d/1du1Ld94b1d2TU4maYIcU-K6v405iWoYTILnO9ipsEXQ/edit?tab=t.0