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Jordan Nanos
Ep. 020 - Anthropic vs OpenAI Usage, Margins, Meta Compute, Future of MSL (Tokenomics) | Crystual Huang, Max Kan, Joey Brookhart, Jordan Nanos
The episode argues that token budgeting mostly hits a small set of power users, while coding remains the dominant driver of AI spend and API revenue. The panel also breaks down Anthropic vs. OpenAI margins, Meta’s compute strategy, and the emerging RL-environment market for frontier-lab data.
[Emergency Episode] Moonshot’s Kimi K3 has Arrived! China has a Frontier Model
Moonshot’s Kimi K3 is presented as a frontier-level model that the hosts rank among the world’s top three, with benchmark strength but a noticeable user-experience gap versus leading closed models. The episode focuses on its 2.8T-scale serving constraints, delayed weight release, higher API pricing, Chinese accelerator strategy, and what the model says about the narrowing open-vs-closed gap.
Ep. 019 - Inside the STEEL Lab: From Package to Transistor (Teardown Lab) | Afzal Ahmad, Andrew Wagner, Jordan Nanos
SemiAnalysis’s STEEL team explains how it tears down chips from package to transistor to infer process, architecture, and packaging details. The episode centers on SMIC’s N3 node and Huawei’s Kirin 9030, highlighting aggressive DUV scaling, SRAM shrink, NPU changes, and the growing challenge of analyzing backside power, gate-all-around, and advanced packaging.
Ep. 018 - Stop Saying Half of 2026 US Datacenter Capacity Is Canceled (Datacenter, Energy) | Jeremie Eliahou Ontiveros, Reyk Knuhtsen, Ellie Holbrook, Jordan Nanos
The episode argues that the viral claim that half of 2026 U.S. data center capacity is canceled rests on a flawed denominator and a mismatch between early-stage announcements and projects actually under construction. The discussion then shifts to behind-the-meter power, gas pipelines, turbines, and why the team thinks AI data center buildouts will keep forcing new generation and infrastructure.
Ep. 017 - DeepSeek V4 and Huawei Ascend NPU Performance (InferenceX) | Kimbo Chen, Cam Quilici, Bryan Shan, Jordan Nanos
DeepSeek V4’s big leap is 1M context via aggressive sparse-attention and KV-cache compression, paired with a mega-MOE/mega-kernel approach to speed expert computation. The episode also compares day-zero support across Nvidia, Huawei Ascend, and AMD, highlighting how early access, kernel fusion, and tooling maturity shape real inference performance.
Ep. 016 - What Unitree's Evolution Means For Robotics (Robotics) | Jordan Nanos, Reyk Knuhtsen, Niko Ciminelli
The episode argues that Unitree’s real moat is not just robot quality, but China’s manufacturing density, fast iteration, and supply-chain depth. The guests think humanoid robotics is still early and messy, yet low-cost robots that are “good enough” for a few useful tasks could create real demand quickly.
Ep. 015 - DG Matrix Explains 800V DC vs Legacy AC Distribution (Datacenter, Energy) | Jordan Nanos, Jeremie Eliahou Ontiveros, Nicolas Bontigui, Haroon Inam
Haroon Inam of DG Matrix argues that 800V DC is becoming necessary as GPU rack power rises beyond what legacy AC distribution can deliver economically. The episode focuses on why voltage, not current, is the key constraint, and how multiport solid-state transformers could make datacenters more flexible, modular, and future-proof.
Ep. 014 - Finding Miscompiles For Fun, Not Profit (AI Infrastructure) | Justin Lebar & Jordan Nanos
Justin Lebar and Jordan Nanos discuss how Lebar found compiler miscompiles using both classic fuzzing and LLM-assisted code review. The episode emphasizes severe x86 and AMDGPU bugs, the difficulty of scaling fuzzers, and the surprising effectiveness—but real cost—of using agents to scan compiler code.