'Nothing Ever Happens' Is Over
Naval Ravikant, Babak Nivi · Naval Podcast
Naval 和 Nivi 做了一期 19 分钟的短播客, 从 Impossible 的 hub-and-spoke 组织谈到 AI 如何替代显式 intranet 和 dashboard, 抬眼看 AI 集中化、 无人机和生物风险的结构性变化, 落到硬件复兴和 "optimism requires creativity" 的处方.
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#hardware
14 集相关 · 14 集已生成
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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."
From Consumer Toys for Pets to War Drone — Yaroslav Azhnyuk, The Fourth Law & Noah Smith, Noahpinion
Yaroslav Azhnyuk · Noah Smith · The Fourth Law / Odd Systems — founder · Noahpinion — writer
Yaroslav Azhnyuk built Pet Cube in San Francisco — "cameras that fling treats to pets" — and then, after Russia's invasion landed on the last flight into Kyiv on Feb 23, 2022, pivoted to building drone autonomy, thermal cameras, and FPV strike drones for the Ukrainian armed forces. With Noah Smith and Latent Space's Brandon, he walks through five levels of drone autonomy, eight dimensions of the autonomous battlefield, why FPVs replaced artillery as "the god of war" (~80% of frontline casualties), and the disquieting arithmetic of China's drone manufacturing scale (4M Ukrainian FPVs/year vs ~4B Chinese capacity).
132. 对星海图创始人高继扬的3小时访谈：鲶鱼、曾国藩、Waymo与Momenta的两面、一只狼与许华哲的离开
高继扬 · 星海图创始人 & CEO
张小珺对星海图创始人高继扬的 3 小时访谈。92 年生人、清华电子系物理竞赛保送、 USC PhD 三年半毕业、Waymo 两年、Momenta 两年作为"鲶鱼"轮换感知/定位/规控/NOA、 23 年 5 月放弃 1000 万美金期权创业。访谈把具身智能创业的"反浪漫"逻辑讲透—— 从曾国藩式的"儒家清流到事功"自我重塑，到智能定义本体的轮式双臂选型， 再到 200~250 元/小时的真实数据成本账、VLM+VLA 双系统、 以及"传播周期决定壁垒"的大厂 vs 创业公司分析。访谈录制时正逢联合创始人许华哲离职。
The $15B Physical AI Company: Simulation, Autonomy OS, Neural Sim, & 1K Engineers—Applied Intuition
Qasar Younis & Peter Ludwig · Co-founders of Applied Intuition (CEO / CTO)
Applied Intuition 给汽车 / 卡车 / 矿车 / 农机 / 防务平台卖"让物理机器变聪明"的技术栈, 18/20 OEM 是它客户, 估值 $15B, 1000 名工程师, 现在在日本跑 L4 无人卡车. 这期把它的三个 bucket (仿真 + 操作系统 + 自动驾驶模型) 全部摊开讲, 还顺手把 "vehicles like pre-Android phones" 这条 类比、neural sim = Gaussian splatting + diffusion、onboard 才是物理 AI 真正瓶颈 这些反直觉点都串了起来.
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。
AI is critical for humanity's survival: Cisco President on the AI revolution | Jeetu Patel
Jeetu Patel · Cisco CPO & President
Cisco CPO/President Jeetu Patel discusses why AI is essential for humanity's survival given declining birth rates, how Cisco connects GPU clusters across data centers as critical AI infrastructure, and shares his six-part framework for building great companies.
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。
The real AI revolution isn't software. It's farms, mines, and trucks. | Qasar Younis
Qasar Younis · Applied Intuition CEO
Applied Intuition CEO Qasar Younis 首次公开访谈。他认为 AI 真正的影响不在软件开发， 而在农业、矿业、建筑和卡车运输等物理行业。对谈涵盖 Physical AI 路线、 中国竞争的误读、创始人的沉默哲学、以及一家近十年未花过融资的 $15B 公司如何运营。
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。