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Artwork for SemiAnalysis Weekly
Nvidia
Openai
Artificial Intelligence
TSMC
GPU Clusters
Cerebras
LLVM
AI Datacenters
Memory (DRAM, HBM)
Claude Code
Inferencex
Vera Rubin NVL72
Chipbook
Goodput
Torchpass
X86
Electricity Prices
Memory (dram/semiconductor Memory)
Semiconductors
Memory

Everything semiconductors and AI

Covering the spectrum

PublishesWeeklyEpisodes16Founded5 months ago
Number of ListenersCategory
Business

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Artwork for SemiAnalysis Weekly

Latest Episodes

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... more

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 ex... more

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 fuzz... more

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 O... more

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Recent Guests

Justin Lebar
Compiler researcher and software engineer
SemiAnalysis / Independent researcher
Episode: Ep. 014 - Finding Miscompiles For Fun, Not Profit (AI Infrastructure) | Justin Lebar & Jordan Nanos
Dylan Patel
AI/semiconductor analyst and commentator
SemiAnalysis contributor
Episode: 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
Chaim Eisenberg
ChipBook team member at SemiAnalysis
ChipBook / Semi Analysis
Episode: Ep. 009 - Using Open Source Data To Drive Investment Decisions (ChipBook) | Chaim Eisenberg, Simi Sherman, Jordan Nanos
Simi Sherman
ChipBook team member at SemiAnalysis
ChipBook / Semi Analysis
Episode: Ep. 009 - Using Open Source Data To Drive Investment Decisions (ChipBook) | Chaim Eisenberg, Simi Sherman, Jordan Nanos
Daniel Nishball
Co-host, AI researcher and engineer
SemiAnalysis
Episode: Ep. 008 Claude Code Psychosis: How SemiAnalysis Is Token Mogging Meta | Dan Nishball, Sam Harshe, Jordan Nanos
Sam Harshe
Co-host, AI researcher and engineer
SemiAnalysis
Episode: Ep. 008 Claude Code Psychosis: How SemiAnalysis Is Token Mogging Meta | Dan Nishball, Sam Harshe, Jordan Nanos
Jeremie Eliahou Ontiveros
Lead data-center energy modeler for SemiAnalysis
SemiAnalysis
Episode: Ep. 004 - The Impact of AI Datacenters On Consumer Power Costs (Datacenter, Energy) | Jeremie Eliahou Ontiveros, Jordan Nanos, Doug O'Laughlin
Cam Quilici
Member of InferenceX team
InferenceX
Episode: Ep. 002 - InferenceX 2.0 Release (Technical Staff) | Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos
Bryan Shan
Member of InferenceX team
InferenceX
Episode: Ep. 002 - InferenceX 2.0 Release (Technical Staff) | Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos

Host

Jordan Nanos
Host of SemiAnalysis Weekly

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#85
Norway/Business
Apple Podcasts
#146
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#183
Singapore/Business

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Ep. 002 - InferenceX 2.0 Release (Technical Staff) | Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos
Q: How do these optimizations translate to real-world costs and deployment?
MTP and related optimizations significantly reduce cost per million tokens and increase tokens per second per user, enabling cheaper operation and potential end-user savings, while still allowing benchmarking to reflect real-world configurations, such as multi-node setups and mixed hardware fleets.
Ep. 002 - InferenceX 2.0 Release (Technical Staff) | Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos
Q: Why is there a 2.0 release and what are the major changes from 1.0 to 2.0 moving to InferenceX?
2.0 expands beyond single-node benchmarks to include disaggregated pre-fill, wide expert parallelism, speculative decoding, and broader hardware support (GB200/GB300) with multi-node testing, aiming for a more nuanced, production-like view of frontier inference and improved throughput and cost metrics.

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Frequently Asked Questions About SemiAnalysis Weekly

What is SemiAnalysis Weekly about and what kind of topics does it cover?

This show centers on deep-dive discussions at the intersection of semiconductors and AI, with a strong emphasis on AI infrastructure, model economics, and data-center hardware. Episodes often feature technical guests and industry analysts who unpack tight topics like token economics, GPU/CPU hardware tradeoffs, wafer-scale designs, and the business implications of AI tooling. A standout trait is the rigorous blend of hands-on engineering detail with market and macro context, frequently anchored by practical frameworks, calculators, and real-world case studies. Listeners can expect a candid, data-driven take on the evolving AI compute stack, where conversations regularly move from hardware design and efficiency to cloud economics, margins, a... more

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3. BG2Pod with Brad Gerstner and Bill Gurley
4. No Priors: Artificial Intelligence | Technology | Startups
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SemiAnalysis Weekly launched 5 months ago and published 16 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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Recent guests on SemiAnalysis Weekly include:

1. Justin Lebar
2. Dylan Patel
3. Chaim Eisenberg
4. Simi Sherman
5. Daniel Nishball
6. Sam Harshe
7. Jeremie Eliahou Ontiveros
8. Cam Quilici

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