How Dassault Systèmes Is Building AI That Understands Physics - Ep. 296

Seeed Studio is a leader in open source robotics, delivering affordable NVIDIA Jetson‑powered arms that put embodied AI into the hands of millions of makers, students, and small businesses. In this episode, Seeed Studio CEO Eric Pan and Head of Robotics Elaine Wu explain how open hardware, the OpenClaw agentic framework, and NVIDIA Isaac Sim are turning robot arms into controllable, teachable agents—and what it takes to bring these physical AI tools into real‑world settings responsibly. 🔬Topics covered: Why open source is the fastest path to accessible robotics How the $200 SOR arm (with Hugging Face) lowers the barrier to embodied AI Training robot arms like a dog: from months of coding to intuitive hand‑guided learning OpenClaw on Jetson: turning natural‑language commands into robot skills Using NVIDIA Isaac Sim and digital twins to bridge simulation and real‑world deployment Building modular robot parts (heads, arms, wheels) instead of monolithic humanoids Chapters: 00:00 – Welcome and introductions 02:00 – From open hardware modules to robotics and edge AI 05:00 – Why open source drives adoption and trust in robotics 09:00 – The $200 SOR arm: open source with Hugging Face 12:00 – Training robot arms like a dog: intuitive, hand‑guided learning 15:00 – OpenClaw on Jetson: text‑to‑robot control 20:00 – Isaac Sim and digital twins: bridging simulation and reality 27:00 – Modular design: heads, arms, wheels instead of humanoids 32:00 – Everyone can participate in physical AI: closing thoughts

As AI factories scale and token costs become a defining competitive variable, the way businesses measure infrastructure ROI needs to change. In this episode, Shruti Koparkar from NVIDIA's Accelerated Computing team breaks down tokenomics—the four-pillar framework of token utility, supply, demand, and monetization—and reveals why NVIDIA Blackwell's architecture delivers 50x more tokens per watt than NVIDIA Hopper, translating to a 35x reduction in token cost. 🔬Topics covered: The four pillars of tokenomics: utility, supply, demand, and monetization Why cost per token beats FLOPS per dollar as an infrastructure metric NVIDIA Blackwell vs. Hopper: 50x more tokens per watt, 35x lower token cost How extreme co-design turns spec-sheet numbers into real-world output Jevons paradox: why lower token cost always drives more GPU demand, not less The four business models for turning tokens into revenue Chapters: 00:00 – Introduction and the four pillars of tokenomics 02:09 – Token value: intelligence, interactivity, and use case mapping 06:32 – Estimating token demand: users, reasoning, and agentic multipliers 10:00 – Token supply and why cost per token is the right infrastructure metric 13:12 – NVIDIA Blackwell vs. Hopper: 50x more tokens, 35x lower cost 14:52 – Extreme co-design for lowest token cost and the NVIDIA Vera Rubin platform 21:10 – How software multiplies hardware performance (8x gains in six months) 23:56 – Token monetization: pricing and business models 26:52 – Jevons paradox and the future of GPU demand

Snap processes more than 10 petabytes of experimentation data every single morning—and with NVIDIA GPU-accelerated Apache Spark on Google Cloud, Snap cut job costs by 76%, reduced memory usage by 80%, and eliminated 120 terabytes of disk spill from its pipelines. Prudhvi Vatala, head of engineering platforms at Snap, joins the NVIDIA AI Podcast to break down how he and his team completely modernized data infrastructure for a social platform serving nearly a billion monthly active users—using NVIDIA cuDF plugin (formerly referred to as NVIDIA RAPIDS plugin) for Apache Spark on Google Kubernetes Engine, with zero application code changes. 🔬Topics covered: How Snap runs A/B tests at planetary scale using rigorous statistical methods like heterogeneous treatment effect detection and variance reduction Why Snap reuses idle inference GPUs between 1–5 a.m. for batch data processing—and how it built a Kubernetes-based platform to do it How NVIDIA cuDF delivered 3x+ speedups on join-heavy Spark jobs with no code rewrites The full business impact: 76% cost reduction, 62% fewer cores, 80% less memory, 120 TB of spill eliminated How a three-way partnership between Snap, NVIDIA, and Google Cloud made it possible in just 8–9 months Chapters: 0:00 Introduction and Snap overview 3:35 What is Snap’s experimentation platform? 4:05 Why experimentation, safety, and privacy are core at Snap 4:52 How A/B testing works at billion-user scale 8:14 Discovering NVIDIA cuDF plugin 9:06 Benchmarking results: join, union, and aggregation jobs 12:00 Reusing idle GPUs overnight via GKE 13:24 Building a bottom-up GPU data platform at Snap 17:48 Results: 76% cost reduction and partnership impact 20:56 Snap’s evolution and what’s next Learn more: NVIDIA cuDF: https://developer.nvidia.com/topics/ai/data-science/cuda-x-data-science-libraries/cudf#accel-apache

LangChain has surpassed 1 billion downloads—and the framework that started as a weekend project is now the harness powering the next generation of production-grade AI agents. In this episode, Harrison Chase, co-founder & CEO of LangChain, breaks down the architecture behind deep agents, explains why systems like Claude Code, Manus, and Deep Research all share the same foundational pattern, and lays out what it actually takes to deploy autonomous agents responsibly in the enterprise. 🔬Topics covered: What is a "deep agent," and why does architecture matter more than ever? How enterprises are (and aren't) embracing autonomous agents LangSmith: observability, tracing, and evaluation-driven development Mixing frontier and open models (NVIDIA Nemotron) in multi-agent systems What's next: async subagents, proactive/always-on agents, agent memory, and agent identity Chapters: 00:00 – LangChain origin story and the deep agent architecture 01:46 – What is a deep agent? 03:31 – Enterprise trust: risk, autonomy, and iteration 04:38 – LangSmith: observability and evaluation-driven development 13:30 – Frontier vs. open models and the Nemotron Coalition 18:10 – What's next: async subagents, agent memory, and agent identity

Generative AI can predict whether a plane takes off—but does it know why? Nicolas Cerisier, VP of 3DEXPERIENCE Platform R&D at Dassault Systèmes, explains how industrial world models go beyond pattern recognition to embed the actual laws of physics, chemistry, and engineering. In this episode of the NVIDIA AI Podcast, he also breaks down Dassault's three virtual companions (AURA, LEO, and MARIE), their 25-year collaboration with NVIDIA, and a stunning real-world use case: helping NIAR rebuild aircraft designs part by part, using AI.

What if one AI brain could run every robot on the planet—a humanoid, a warehouse arm, and a dog-like inspection bot—all at once? That's not a thought experiment. That's what Skild AI is building right now. Deepak Pathak (CEO and Co-Founder) and Abhinav Gupta (President and Co-Founder) of Skild AI join the pod to break down Skild Brain—a universal, general-purpose AI model designed to power robots of any form factor, tackling any task, from a single shared intelligence.

What happens when you combine AI with quantum computing? NVIDIA's Nic Harrigan joins the AI Podcast to break down the state of quantum, explain why error correction is the pivotal challenge, and reveal how NVIDIA Ising—the world's first open AI model family for quantum—is changing the game. 🔗 Resources mentioned: ► Read our NVIDIA Ising announcement  ► Learn more about NVIDIA Ising  ► Learn more about NVIDIA Quantum Computing Chapters: 0:00 Intro 0:55 What is quantum computing? 4:00 Qubits, noise, and error correction 5:26 How AI helps quantum error correction 10:57 Applications: drug discovery and materials 15:33 NVIDIA Ising announcement 20:35 Scaling quantum hardware 27:31 Algorithm development with generative AI

Enterprises are moving from AI pilots to full‑scale AI factories that turn data into trusted digital intelligence. Red Hat CTO Chris Wright and NVIDIA’s Justin Boitano unpack the "five‑layer cake" AI factory stack, from accelerated hardware and hybrid cloud infrastructure to models, agents, and production‑grade governance.

AI is reshaping electricity demand. What does increased demand, and the shape of that demand, mean for the electric grid? Ben Sooter, Director of R&D at EPRI joins the podcast to explain why most of an AI model’s lifetime energy use comes from inference rather than training, and how micro data centers located near underutilized substations can help deliver low‑latency AI services while strengthening grid resilience.

Alibaba.com president Kuo Zhang discusses how AI agents like Accio are reshaping global trade. He shares insights on automating complex B2B sourcing, compressing weeks of work into minutes, lowering barriers for solo entrepreneurs and SMEs, and what AI-native commerce will mean for the next decade.