SemiAnalysis Weekly

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

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.

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

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

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

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