SemiAnalysis Weekly

Jordan Nanos, Doug O'Laughlin

Everything semiconductors and AI Covering the spectrum

  1. 5 gg fa

    Ep. 020 - Anthropic vs OpenAI Usage, Margins, Meta Compute, Future of MSL (Tokenomics) | Crystual Huang, Max Kan, Joey Brookhart, Jordan Nanos

    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 Conversations with Enterprises on Token Spend: https://newsletter.semianalysis.com/p/tokenbudgeting-our-conversationsMeta Compute: Everyone Wants To Be A Neocloud: https://newsletter.semianalysis.com/p/meta-compute-everyone-wants-to-beAnthropic 3Q26 Profit Over $1B: The Anthropic IPO Financials Sneak Peak: https://newsletter.semianalysis.com/p/anthropic-3q26-profit-over-1b-theThe Future of Meta Superintelligence: A 1 Year Progress Update: https://newsletter.semianalysis.com/p/the-future-of-meta-superintelligenceComplete Launch Kit

  2. 5 gg fa ·  Contenuti extra

    [Emergency Episode] Moonshot’s Kimi K3 has Arrived! China has a Frontier Model

    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

  3. 6 gg fa

    Ep. 019 - Inside the STEEL Lab: From Package to Transistor (Teardown Lab) | Afzal Ahmad, Andrew Wagner, Jordan Nanos

    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

  4. 9 lug

    Ep. 018 - Stop Saying Half of 2026 US Datacenter Capacity Is Canceled (Datacenter, Energy) | Jeremie Eliahou Ontiveros, Reyk Knuhtsen, Ellie Holbrook, Jordan Nanos

    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 responding 20:24 Signed deal to powered GPUs 23:44 Hyperscaler market share 27:49 How SemiAnalysis tracks data centers 31:34 What behind the meter means 33:31 The gas turbine supply chain 38:22 Peak turbine 42:54 Solar, batteries, and nuclear 46:13 Final thoughts 48:20 Favorite projects Referenced: Stop Saying Half of 2026 US Datacenter Capacity Is Canceled: https://newsletter.semianalysis.com/p/stop-saying-half-of-2026-us-datacenter US Grid Constraints: Towards 40GW+ of Behind-The-Meter Datacenter by 2028?: https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw

  5. 1 lug

    Ep. 017 - DeepSeek V4 and Huawei Ascend NPU Performance (InferenceX) | Kimbo Chen, Cam Quilici, Bryan Shan, Jordan Nanos

    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 attention and KV cache reduction(03:04) Day zero runtime support challenges(05:34) What Mega MoE actually is(08:38) Downsides of fusing kernels(10:25) MegaKernel benchmark claims(12:59) AMD FP4 optimization gains(15:14) Compounding step by step improvements(17:59) vLLM versus SGLang competition(19:34) Open source vs vendor libraries

  6. 23 giu

    Ep. 016 - What Unitree's Evolution Means For Robotics (Robotics) | Jordan Nanos, Reyk Knuhtsen, Niko Ciminelli

    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 analysis.Article: https://newsletter.semianalysis.com/p/chinas-unitree-will-dominate-globalTimestamps:00:00 — Intro, deployment reality vs. hype02:39 — Unitree Business: why go public, margins, pricing05:48 — Parallels to DJI and BYD, economies of scale as China's moat16:52 — When is Claude Code moment for robotics18:38 — Real use cases and future demand shocks26:39 — Shenzhen and the humanoid BOM35:56 — The bear case: who actually buys them?42:17 — Can the US compete?

  7. 15 giu

    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

  8. 4 giu

    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

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Descrizione

Everything semiconductors and AI Covering the spectrum

Informazioni

  • Creatore
    Jordan Nanos, Doug O'Laughlin
  • Anni di attività
    2026
  • Puntate
    21
  • Classificazione
    Contenuti espliciti
  • Copyright
    © Jordan Nanos, Doug O'Laughlin
  • Sito web del podcast
    SemiAnalysis Weekly

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