
aipodcast@nvidia.com
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Episodes: 304
Frequency: Weekly
Rating: 4.5/5.0
Estimated listeners: 1k-10k
Gender skew: Neutral
Location: USA
YouTube: 2.0M subscribers
Instagram: 3.0M followers
aipodcast@nvidia.com
For verified host and producer emails, sign up to view.
Elaine Wu - Open Hardware Robotics At Scale, Openclaw Agentic Framework, Training/controlling Robot Arms With Language Commands, Simulation-to-reality Workflows
Eric Pan - Open Source Embodied Robotics, Agentic Frameworks For Robot Arms, NVIDIA Isaac Sim/digital Twins, Responsible Deployment Of Physical AI
Shruti Koparkar - AI Tokenomics Framework, Token Utility/supply/demand/monetization, Measuring Infrastructure ROI With Cost Per Token
Prudhvi Vatala - Gpu-accelerated Apache Spark On Google Cloud, Experimentation Infrastructure At Scale, Cudf Plugin Performance Results, Privacy/safety Considerations
Everyone Can Build a Robot: Open Source Embodied AI With Seeed Studio | NVIDIA AI Podcast Ep. 300
May 27, 2026
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. 🔬T...
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 pill...
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—usi...
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