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Every hybrid bonder on the market today brings the die to the wafer. ASML's patents bring the wafer to the die.Sherry Zhang and Howie join Jordan Nanos (@jordananos) to dig into the patents behind ASML's die-to-wafer hybrid bonder, why a track-based design could beat Besi on alignment and particle control, and why HBM won't adopt die-to-wafer hybrid bonding before 2029. The team also looks at how China is using hybrid bonding to keep scaling past its lithography limits, and what larger photomasks mean for High-NA EUV.Chapters:0:00 The Patent Tip-Off1:53 Hybrid Bonding 1014:06 Die-to-Wafer vs Wafer-to-Wafer5:43 Back End vs Front End8:05 Why HBM Waits Until 202913:45 Everything Comes to the Die18:26 Alignment and Cleanliness22:48 Bonding Instead of Shrinking25:49 China's Toolmakers Step Up28:25 Bigger Masks for High-NA33:29 Two Tool Stacks?Read the full article: ASML Is Building a Hybrid Bonding Tool!https://newsletter.semianalysis.com/p/asml-is-building-a-hybrid-bonding

Brad Gerstner put our 43 gigawatt forecast for 2027 on All-In, then again in his BG2 sit-down with Jensen, and called it too aggressive. The consensus counter sits near 25 GW, built on moratorium headlines and grid connection fears. Jordan Nanos (@JordanNanos) sits down with Jeremie Ontiveros (@JeremieEO) and Reyk Knuhtsen (@robotknower), who have been tracking the same capacity curve since March 2024, and it has barely moved across every model revision. So they aren't cutting it. They're raising it. Read the article: https://newsletter.semianalysis.com/p/datacenters-2027-the-year-of-acceleration 0:00 Cold Open 1:17 The 2027 Forecast Debate 2:21 Model Track Record 5:27 Where the Pushback Comes From 8:16 Delays and Haircuts 12:31 The Top 40 Sites 15:23 Leading Indicators 20:40 Modular Builds 25:41 Chips vs Datacenters 27:53 Responding to Brad Gerstner

GLM 5.3 only attends to the tokens that matter. It still has to keep every one of them. Kimbo Chen (@kimbochen) and Wenyao Gao (@gao_wenyao) join Jordan Nanos (@JordanNanos) to dig into AMD catching NVIDIA on InferenceX, how post-training alone pushed an open-weight model within reach of Mythos, and whether Z.ai quietly tuned it for Chinese chips. The team also looks at how GLM 5.3 became a fallback when Claude and GPT refuse security work. Chapters: 0:00 Cold Open 0:37 Sparse Attention 101 4:46 HiCache and HiSparse 10:13 AMD Catches NVIDIA 12:54 Capacity vs Bandwidth 18:44 The Frankenstein Flash 20:09 Mythos-Class From China? 22:52 Post-Training Pipeline 32:32 Chinese Chips, Day One 39:54 GLM 5.4 and Beyond Read the full article: How GLM 5.3 Sparse Attention Affects HBM Memory Usage https://newsletter.semianalysis.com/p/sparse-savings-persistent-demand-inside-glm53

H100 rental prices went up, on a GPU that should be sliding down its depreciation curve. That curve is what lenders underwrite. Daniel Nishball (@dnishball) kicks off our new Compute Capital Markets group by following the money through Nvidia's $497B backstops, ClusterMAX as a credit signal, and whether each token is profitable. Jordan Nanos (@JordanNanos) brings the rental data. AI debt is about to pass auto loans. 0:00 Opening 1:48 Nvidia's Backstop Universe 6:22 The $11T Funding Wall 17:25 Balance Sheet as a Service 26:27 ClusterMAX as Credit Risk 31:51 Why H100 Prices Rose 43:09 Token Profitability 58:05 You Can't Go Back 1:07:25 AI Debt vs Auto Loans 1:11:14 Inside Nvidia's Backstops

Pay Anthropic $200 a month and you can pull $12,000 of Opus 5.5 tokens at API pricing. Max Kan (@maxkan) and Andrew Megalaa bought every plan, OpenAI, Meta, xAI, MiniMax, Moonshot, Z.ai, Cursor, and measured every token type to find the ceiling. The catch isn't subsidy. Anthropic still books 50 to 60% gross margins on these plans, which means the API itself is the price gouge. 0:00 Cold Open 1:03 Subsidies and Credits 5:23 Token Types and Cache 14:08 Model Value Charts 20:51 Test Methodology 26:54 Other Plans 31:26 Utilization and Limits 36:24 Token Efficiency 41:24 Open Models 44:22 Cyber and Capture Read More: https://newsletter.semianalysis.com/p/anthropic-subscriptions-offer-5x

Jordan (@JordanNanos) and Abhilash (@AbhilashJain17) discuss the clients SemiAnalysis consulting serves, custom projects like the inference simulator, how client work turns into research products, neocloud due diligence, and why SLAs decide who gets funded. They also discuss Feynman specs, de-specced memory, and a 20-trillion-parameter OpenAI model. 0:00 Intro and Overview 2:27 Client Types 5:24 Custom Projects 9:47 Strategy Work 12:27 Consulting to Products 14:33 Neocloud Due Diligence 18:40 SLAs and Insurance 22:32 Risk Assessments 26:45 Build vs Buy 29:19 Favorites and Outlook

The InferenceX team is back after two months. Jordan Nanos (@JordanNanos) is joined by Cam Quilici (@noslawextratost), Bryan Shan, and Alec Ibarra to discuss Engram and DRAM offloading, the AgentX benchmark and its profit calculator, TPU v7 on InferenceX, Vera Rubin vs Blackwell, fast tokens, and TileRT on NVIDIA GPUs. 0:00 Intro 0:31 Engrams 12:27 AgentX Benchmark 16:36 Profit Calculator 23:40 TPU v7 32:03 Vera Rubin 40:49 Fast Tokens 49:33 TileRT 54:00 Chip Startups 1:01:31 Wrap Up

ClusterMAX 3.0 is here. Sam Harshe (@sharshe02) and Pratt Bhatt (@PrathmeshBhat19) join Jordan Nanos (@jordannanos) after months of testing 77 GPU clouds. Some moved up, some moved down. Health checks, cache hit rates, NCCL recipes, agentic coding on clusters, a Linux kernel bug, neocloud security, SLAs and financing, NVIDIA's backstops, five-year-old H100s, and hosted RL training. Read the full ClusterMAX 3.0 report: https://newsletter.semianalysis.com/p/clustermax-30-the-industry-standard ClusterMAX inquiries: clustermax@semianalysis.com 0:00 Cold Open 1:32 The Rankings 6:58 Health Checks 13:26 Endpoint Cache Misses 16:40 Networking and NCCL 22:52 Vibe-Coded Infra 31:27 Bug Hunting 38:36 Security 45:43 Financing Neoclouds 56:33 Hosted Training and RL

More than 300 towns, cities, and counties have voted to pause data centers in the past 18 months. Maya Barkin (@mayabarkin) and Reyk Knühtsen (@robotknower) from the SemiAnalysis Data Center, Energy, and Industrials team join Jordan Nanos (@jordannanos) to explain what a moratorium actually is, how they mapped every one of them against the US project pipeline, and why only three projects are being delayed. Plus: the SpaceX de-annexation in Brownsville, New York vs Texas, why politicians love a moratorium, and the surprising amount of alpha in anti-data center Facebook groups. Jeremie Eliahou Ontiveros (@JeremieEO) drops in from Bangkok. Read More: https://newsletter.semianalysis.com/p/everyone-says-datacenter-moratoriums 0:00 Intro 0:36 What Is a Moratorium 4:34 300 Moratoriums, 3 Projects 10:18 Behind the Meter Wins 14:04 Eight Conditions to Delay 17:06 The SpaceX De-Annexation 20:03 Cheap Political Signaling 32:03 New York vs Texas 41:14 City Council Alpha 46:32 Advice for Developers

Emergency episode. Doug O'Laughlin (@Fabknowledge) , Joey Brookhart (@SaasquatchC), and Max Kan (@maxkan) join Jordan (@JordanNanos) to talk through Dario's "We Must Pace the Frontier" blog and the responses from Sam Altman and David Sacks. Does pacing actually mean less compute, or a lot more? Plus the Hugging Face incident, Moonshot serving Claude, and the security behaviour that keeps them up at night. 0:00 Cold Open 2:50 Glorified Neoclouds 8:21 Pacing the Frontier 12:53 Safety Eats Compute 20:38 Hugging Face Lessons 30:52 Moonshot Serving Claude 36:37 The Coxson Resignation 38:27 Two-Year Predictions 49:19 Regulation Under Trump 57:21 Hotter or Cooler

NVIDIA previewed Rubin Ultra at 1TB of HBM per package. The part that ships will carry 192GB. Myron Xie and Jordan Nanos get into how the design fell that far, why the reason is supply and not performance, and why shipping less memory per chip might be the right call anyway. For the first time in recent memory, NVIDIA's next flagship will have less capacity than the one it replaces. Read More: https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi

Traditional data center builds ran 18 to 24 months, sometimes past three years. Modular construction cuts that to 12. Jordan Nanos (@JordanNanos) sits down with Eric Wen, Nigel Chiang, and Nico Bontigui to map what modular actually means, from MEP heavy skids to full containerized builds. They break down why the bottleneck moved from construction time to labor, why welders and electricians are the real constraint in Abilene, and how time to power translates directly into tokens at 40 to 100 million dollars per megawatt. Subscribe for weekly SemiAnalysis coverage on data center construction, prefabrication, and AI infrastructure. 0:00 Cold Open 1:09 What Is Modular 4:03 Why Go Modular 5:55 Time to Power Math 9:04 Labor Wage Data 13:51 Two Kinds of Bottlenecks 18:24 Site, Shell, System 22:31 Who Owns the Risk 26:59 Trucks and Insurance 30:53 Who Supplies the Modules 35:50 Commissioning Reality 41:39 Vendor Map and Wrap Read More: The Wild Wild West Of LEGO Datacenters:…

This week Doug, Sam and Jordan discuss the OpenAI vs HuggingFace security incident and our recent article on Neocloud security ahead of ClusterMAX 3.0 Full article: https://newsletter.semianalysis.com/p/most-neoclouds-suck-at-security 0:00 Cold Open 0:57 Neocloud Security 4:38 The Hugging Face Hack 11:47 Agent Swarm Behavior 14:47 Obliterated Models 20:37 Security as a Service 24:30 What the Data Shows 33:08 Attacker Asymmetry 40:55 Nothing Ever Happens 45:22 CMAX Audit

This week Bryan, Myron and Jordan discuss our recent article on OpenAI Jalapeño. They cover the performance, architecture, programming model, implications for NVIDIA and more.1:05 Jalapeno Overview4:22 Tokens Per Megawatt9:15 Benchmark Caveats13:48 The CUDA Moat21:12 How OpenAI Did It32:13 Samsung HBM442:10 AI-Designed Silicon49:24 Architecture Deep Dive56:58 Doom and Wrap Full Article: ⁠https://newsletter.semianalysis.com/p/openai-jalapeno-better-than-nvidia

PJM overpaid $12 billion across two capacity auctions, and the same modeling error is set to repeat in an upcoming emergency auction. Robert Boswall (@RobertBoswall) and Jordan Nanos (@JordanNanos) break down how the largest grid in America, 13 states and 66 million people, inflates demand while constraining supply. The result is scarcity pricing that ratepayers absorb, not the data centers driving the narrative. Of the $63 billion spent across four auctions, Boswell estimates $12 billion was avoidable, split $7 billion and $5 billion across the 2025-26 and 2026-27 auctions. This is the mechanics behind every headline blaming AI for rising power bills. Subscribe for weekly analysis on grid design, capacity markets, and AI energy demand! 0:00 Cold Open1:33 What Is PJM3:13 Capacity Auctions7:39 The $12 Billion12:33 How Plants Get Paid15:15 The Winterization Gap21:17 Data Center Demand26:41 The Emergency Auction30:00 Board Overrules Members40:32 Turning It Around Article:…

Dylan joins the pod today, recorded live from our SF office. Jordan and Dylan discuss SemiAnalysis's AI spend, the model that escaped during training, AI rollups eating private equity, new accelerators vs NVIDIA, and whether the pod gives away too much information for free.0:00 Cold Open5:25 SA AI Spend9:29 AI Performance Reviews10:40 AI Rollups13:47 The Agent Moment15:28 The Escaped Model18:11 Model Exponential22:52 Compute and Chips30:18 Open Models32:00 ADHD and AI

OpenAI and Anthropic are adding close to $30 billion of ARR per month, and the driver is gross margin expansion, not new compute. Jeremie Eliahou Ontiveros (@JeremieEO) and Reyk Knuhtsen (@robotknower) join Jordan Nanos (@JordanNanos) to trace the $100 million per megawatt per year figure from real inference workloads on GB200 and GB300 up through SemiAnalysis simulation. The Google deal prices GB300 capacity near $14 an hour against a $3 average, a premium justified by a 90 day cancellation clause and the ability to turn on megawatts immediately. From there the conversation moves to SpaceX's 10 gigawatt ambition, the sites and supply chain needed to hit it, the permitting playbook, and why Microsoft becomes the largest offtaker. The bull and bear cases both get airtime, followed by a Hugging Face security scare to close. Subscribe for weekly coverage of tokenomics, datacenter energy, and AI compute economics. CHAPTERS: 0:00 – Intro 0:47 – The $100M Thesis 3:59 – Pricing & Google's Deal 10:38 – Training vs…

Jeff Dean and the Gemini leads are leaving Google. Jon Y (@asianometry) makes his FIRST appearance to unpack the exodus with Doug O'Laughlin (@fabknowledge) and Jordan Nanos (@JordanNanos). The crew debates the Demis CEO question, whether Google acquired its innovation or invented it, and how the Bell Labs comparison reads once disruption arrives. First engineers who spent thirty-five years in one job now leaving for passion projects.The episode opens by revisiting the 2024 Dwarkesh clip where Jon questioned whether GPT-5 would be any good. Two years later the verdict is mixed: GPT-5 was a dud, 5.2 was ass, but 5.6 is strong and GPT-6, codenamed Doug, is rumored to write well. Then the Chinese transceiver ban, where the West depends on a supply chain China already owns. 00:00 Intro 01:02 Was GPT-5 Good? 05:00 The Transceiver Ban 08:57 Everyone Leaves Google 12:56 Google's L Culture 19:20 Do Legends Matter? 23:44 The Protestant Church 28:44 Elon's $1T Pull-In 30:55 Roll Your Own Software 38:17 Start With…

Doug is back this week! Timestamps: 00:00 Market Update 07:02 Comparing to Past Bubbles in Taiwan and Korea 10:08 Memory Prices, LTAs, and Market Cycles 18:11 Future Demand for AI and Model Usage 37:40 Scaling Laws and Supply Constraints 44:45 Financial and Capital Constraints in Tech Expansion 50:57 Geopolitical Risks, Policy Impact, Long Term Outlook

AI and datacenter CapEx hits $11 trillion cumulatively from 2024 to 2029, and $7.1 trillion of that needs funding, roughly 75% debt financed. Dan Nishball (@dnishball), Zane Fong (linkedin.com/in/zanefongzq), and Kang Wen Cheang (linkedin.com/in/cheangkangwen) sit with Jordan Nanos (@JordanNanos) to break down the AI project Trinity: capital, offtake, and data centers. Today only one deal reliably clears the lending bar, a five-year offtake from an investment-grade hyperscaler. Everything else struggles to finance.The crew explains how NVIDIA backstops are structured, how lenders price GPU loans against them, and what NVIDIA actually does with GPUs it takes back. AI debt financing is on track to become the second-largest US asset-backed market behind the $13 trillion mortgage market. Subscribe for weekly analysis on semiconductors and AI infrastructure. References: https://newsletter.semianalysis.com/p/nvidia-gpu-debt-backstop-unleashes CHAPTERS: 00:00 Intro| 01:23 The $11T Funding Problem 09:12 Startups…

Coding drives over 70% of lab API revenue, and token austerity policies mostly miss the point. Crystal (@crystalthegg), Max Kan (@maxkan), and Joey Brookhart (@SaasquatchC) break down why blocking teams from Opus saves nothing, while power users at the 99th percentile burn $100k per employee per year. Jordan (@Jordannanos) and the team run break-even math on the Max plans and Anthropic's margins. 00:00 Intro00:53 Token Budgeting: Maxing vs Austerity03:05 Coding Eats the Token Market04:49 The ROI Question06:42 Subscriptions vs API Pricing09:08 Break-Even Math on the Max Plans10:42 Anthropic's Profit Margins12:07 Consumer vs Enterprise Mix16:04 The Two-Horse Race18:45 Codex App vs CLI20:41 Who Comes in Third?23:14 Clawbacks and the SpaceX Playbook26:59 Meta's NeoCloud Backstop29:58 Token-as-a-Service Market Forecast32:03 Hyperscalers vs Inference Startups38:50 MSL and the RL Scaling Law41:58 How to Build a Five-Figure RL Task47:10 Vibe Checks48:13 The $400B Anthropic BetReferenced:TokenBudgeting: Our…

A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed.Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days.New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in the game. 00:00 Intro 00:11 Is Kimi K3 the Third Best Model? 04:04 Why Delay the Weights? 05:30 2.8T Parameters and Serving Constraints 06:48 Frontier Margins and the 3x Price Hike 11:10 New Architecture, What Comes Next 14:09 Will Open Source Catch Closed? 19:51 Built for Chinese Accelerators 22:57 The Harness Is the Product 28:49 We're Still Early

SMIC's N+3 node shrank the M0 layer over 15% and cut SRAM area 10 to 20%, all without EUV. SemiAnalysis built a lab just for that; Andrew Wagner and Afzal Ahmad walk Jordan Nanos (@JordanNanos) through the STEEL teardown of Huawei's Kirin 9030, from package to transistor. They explain die shots, FIB and TEM cross sections, NPU discovery, cell height, and standard cell libraries. Then backside power, GAA, and where SMIC goes next. 00:00 Intro: The STEEL Teardown Lab01:02 What Is a Teardown?02:17 Who Uses Teardown Data03:22 SMIC N+3 and the Kirin 903005:02 Inside the Lab: Sourcing to Silicon09:10 Die Shots Explained12:35 The NPU Discovery15:42 Scaling Without EUV17:57 FIB, SEM, and TEM Cross Sections20:59 Cell Height and Transistor Shrink23:56 Standard Cell Libraries26:22 Export Bans and Huawei's Response27:40 What's Next: Backside Power and GAA32:43 Data Center GPUs and Logic Folding34:28 Closing Thoughts Read More: https://newsletter.semianalysis.com/p/steel-smic-n3-teardown

Bloomberg said half of 2026 US data center capacity is delayed. The SemiAnalysis Data Center, Energy, and Industrials team pulled the underlying report and found a broken denominator. Amazon alone built 4GW in 2025 and is adding 5GW plus in 2026. CoreWeave adds a gigawatt, all under construction. Jeremie Eliahou Ontiveros (@JeremieEO), Reyk Knuhtsen (@robotknower), and Ellie Holbrook join Jordan Nanos (@JordanNanos) as they walk through why the number is wrong and what the real forecast shows. The team covers behind the meter power generation reaching 40GW by 2028, Oracle's New Mexico problem, and the gas turbine supply chain that is running toward peak. They break down the three types of data center delays, how OEMs are responding, and where solar, batteries, and nuclear fit. 00:00 Intro 00:51 The "half of capacity is canceled" myth 04:43 Why early stage projects get canceled 08:06 Three types of data center delays 08:40 Oracle's New Mexico problem 13:47 Behind the meter: 40GW by 2028 18:50 How OEMs are…

DeepSeek V4 claims a 100x KVcache reduction versus a standard MoE model, hitting 1M context length through compressed sparse attention and heavily compressed attention. Kimbo (@Kimbochen), Cam Quilici (@noslawextratost), Bryan Shan join Jordan Nanos (@JordanNanos) to break down what changed from V3, why the new MHC dimension tripped up NVIDIA on day zero, and how Mega MoE fuses compute and communication into a single kernel. The vLLM versus SGLang NDA access gap and the Huawei day zero optimization guide circulating on Twitter.The crew walks through the InferenceX article on going from day zero to day 43 support and what that grind actually looks like across different hardware. Subscribe for weekly semiconductor and AI infrastructure analysis from the SemiAnalysis team. Referenced:DeepSeekV4 1.6T Day 0 to Day 43 Performance Over Time - GB300 NVL72, Huawei, MI355X, B200: https://newsletter.semianalysis.com/p/deepseekv4-16t-day-0-to-day-43-performanceChapters: (00:00) DeepSeek V4 vs V3 changes(01:00) Sparse…

Unitree is going public, boasting 67% gross margins on its humanoid robots. Jordan Nanos (@JordanNanos), Reyk Knuhtsen (@robotknower), and Niko Ciminelli discuss how the Chinese company achieves this through aggressive pricing, rapid iteration, and a focus on "good enough" hardware for the research and hobbyist markets. This strategy allows Unitree to dominate, much like DJI and BYD did in their respective fields.The discussion explores the reality of humanoid robot deployment versus market hype. While industrial applications are in their "baby days," Unitree's approach leverages economies of scale to create a significant moat, challenging US competitors to match their production volume and cost efficiency. The team analyzes if the US can truly compete with China's manufacturing might in the emerging robotics sector.Join SemiAnalysis Weekly for expert insights into the semiconductor, AI infrastructure, and robotics markets. Subscribe for deep dives into AI supply chain, chip economics, and market…

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…

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…

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…

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…

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…

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…

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…

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…

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. Chapters 00:00 Introduction and Guest Credibility 03:44 Memory Constraints and Market Impact 07:14 Elasticity of Supply and Economic Analogies 10:00 AI Adoption in Personal and Enterprise Life 14:57 Global Distribution of AI Usage and Tokens 20:00 Consumer vs Enterprise AI Use Cases 24:56 Future Market Growth and Adoption Scenarios 29:48 AI's Impact on GDP and Economic Measurement 40:12 AI and the Future of Work and Productivity 50:02 Global Workforce and AI's Economic Effects 01:00:03 Building AI-Driven Dashboards and Tools 01:09:59 Reevaluating GDP and Economic Value of AI 01:19:56 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 Prices 02:15 Market Dynamics: PJM vs. ERCOT 04:56 Supply and Demand Challenges 08:02 Thermal Accreditation and Market Reforms 11:05 Future of Energy Supply and Coal Retirements 17:13 The Future of Coal Power Plants 18:27 Balancing Energy Prices and Consumer Expectations 19:29 Customized Tariffs and Their Impact 20:28 Investment Commitments and Grid Reliability 22:30 Negotiation Timelines for Data Centers 24:32 Financing Mismatches in Energy Projects 25:10 The AI Boom and Its Impact on Energy 27:13 Anthropic vs. OpenAI: The Revenue Race 29:15 The Role of Government in AI Adoption 32:00 Demand…

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