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How to ship hardware in the AI era | Caitlin Kalinowski (Apple, Meta, OpenAI)

Caitlin Kalinowski · Hardware leader — ex-Apple (MacBook Pro/Air), ex-Meta (Quest, Rift, Orion AR), ex-OpenAI (robotics)

Caitlin Kalinowski has shipped flagship hardware at three legendary companies — the original unibody MacBook Pro and MacBook Air at Apple, the Quest / Rift / Orion AR program at Meta, and most recently OpenAI's robotics division (which she publicly resigned from over the "department of war" deal). She walks Lenny through why hardware is fundamentally different from software ("we only get to compile four or five times — ever"), the 5-layer supply-chain stack that's been outsourced over 25 years, Palmer Luckey's drones-vs-aircraft-carriers argument, the impulse equation that governs robot safety, the "memory-price meteor" coming for every physical AI startup, and the four hardware design principles she carries from Apple to Meta to OpenAI.

Jensen Huang – Will Nvidia's moat persist?

Jensen Huang · Nvidia CEO

Dwarkesh 花了两小时把 Jensen 逼到角落里问:如果 AI 商品化软件,Nvidia 会不会 也被商品化?Jensen 的答案是 "electrons → tokens 的转换" 本身就是护城河,并一路 反驳 TPU / ASIC 叙事、承认自己没能早投 Anthropic 的 "my miss",最后跟 Dwarkesh 就 China 出口管制打了一场 40 分钟的激烈辩论 —— "you're not talking to somebody who woke up a loser."

State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490

Sebastian Raschka, Nathan Lambert · 《Build a Large Language Model (From Scratch)》作者 + AI2 研究员 / RLHF 书作者

Lex Fridman 请来两位"一线做过模型、也写过书"的研究者做 2026 年初的 AI state-of-the-art 盘点: Sebastian Raschka 从 GPT-2 一路手撕到 Qwen3 / Gemma 3, 最擅长从架构里读故事;Nathan Lambert 是 AI2 研究员、RLHF 书作者、atom 项目 发起人,frontier 与 open-source 两边都站过。两人聊了 DeepSeek 时刻、Opus 4.5 神话、RLVR 的"假 aha"、scaling 的三个轴、AI 2027 的时间线推后、Anthropic $1.5B 和解、CUDA 的真护城河、atom project,一直到 100 年后世界的样子。

Jensen Huang: NVIDIA — The $4 Trillion Company & the Future of AI

Jensen Huang · NVIDIA CEO

Jensen Huang 把 NVIDIA 的打法讲成一条推理链:从 extreme co-design 到四条 scaling law,从 CUDA 在 GeForce 上的存在主义赌博到 agentic 时代的 "iPhone of tokens"。他也谈到 TSMC、中国、 供应链、能源瓶颈,以及"intelligence is a commodity"的人生哲学。

Elon Musk – "In 36 months, the cheapest place to put AI will be space"

Elon Musk · CEO of Tesla, SpaceX, xAI

Elon Musk 与 Dwarkesh Patel 长达 3 小时的深度对谈,涵盖太空 AI 数据中心(36 个月内最经济)、 Starship 每小时一次发射、月球质量驱动器、Terafab 自建芯片厂、Optimus 机器人的递归指数增长、 中美制造业竞争,以及 xAI "understand the universe" 使命与 AI 安全。

Dylan Patel — The single biggest bottleneck to scaling AI compute

Dylan Patel · CEO, SemiAnalysis

SemiAnalysis CEO Dylan Patel walks through the entire AI compute supply chain, from $600B hyperscaler capex to the 3.5 EUV tools needed per gigawatt. He explains why semiconductors — not power — are the real bottleneck, and what that means for the US-China AI race, memory prices, and consumer electronics.

Fixing GPU Starvation in Large-Scale Distributed Training

Kashish · Uber · ML Infra · Marketplace Matching Lead

Kashish (Uber ML infra, ex-Google YouTube Ads) walks Demetrios through a Sherlock-Holmes-grade Petastorm bug—GPU cluster stuck at 15-20% utilization, six debugging steps, two layers of bottleneck, and finally a "double bottleneck" reveal: PyArrow→NumPy translation was silently eating the headroom. Plus serving's latency-vs-utilization war, the reproducibility cost of parallelism, and a live diagnosis of a friend's slow DGX Spark.

How GPT, Claude, and Gemini are actually trained and served – Reiner Pope

Reiner Pope · MatX CEO, ex-Google TPU

Reiner Pope(MatX CEO)在 Dwarkesh 的黑板课式访谈里,从几条 roofline 方程一路推到: 最优 batch size = 300 × sparsity、20ms 的"火车"调度、MoE 必须挤进一个 rack、 Ilya 为什么说 pipelining 不智、RL 时代模型被过度训练 100 倍、Gemini API 价格如何 泄露 ~2KB/token 的 KV cache 大小,以及神经网络和密码学之间的对偶关系。