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NVIDIA AI Podcast

NVIDIA AI Podcast

By: NVIDIA
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Explore how the latest technologies are shaping our world, from groundbreaking discoveries to transformative sustainability efforts. The NVIDIA AI Podcast shines a light on the stories and solutions behind the most innovative changes, helping to inspire and educate listeners. More information: https://ai-podcast.nvidia.com/All rights reserved e203f610-4315-11f0-9656-27588e6ca9e9
Episodes
  • Inside AI Tokenomics: How to Profitably Turn Tokens Into Business Value | NVIDIA AI Podcast Ep. 299

    Inside AI Tokenomics: How to Profitably Turn Tokens Into Business Value | NVIDIA AI Podcast Ep. 299
    May 21 2026
    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
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    33 mins
  • Snap’s Secret to Processing 10 Petabytes a Day: GPU-Accelerated Spark | NVIDIA AI Podcast Ep. 298

    Snap’s Secret to Processing 10 Petabytes a Day: GPU-Accelerated Spark | NVIDIA AI Podcast Ep. 298
    May 13 2026
    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
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    24 mins
  • Harrison Chase of LangChain on Deep Agents, LangSmith, and Earning Trust | NVIDIA AI Podcast Ep. 297

    Harrison Chase of LangChain on Deep Agents, LangSmith, and Earning Trust | NVIDIA AI Podcast Ep. 297
    May 6 2026
    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
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    25 mins
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