Noah Kravitz
Noah Kravitz is a technology journalist, interviewer, consultant, and host/co-producer of NVIDIA's AI Podcast, where he interviews AI researchers, founders, and business leaders about applied AI, deep learning, enterprise AI, and emerging technology trends.
Low-Cost Robot Arms Let Non-Specialists Train Physical AI
On NVIDIA’s AI Podcast, Seeed Studio CEO Eric Pan and head of robotics Elaine Wu make the case that open-source, Jetson-powered robot arms can move embodied AI beyond specialist industrial settings. Their argument is that low-cost hardware, frameworks such as OpenClaw and LeRobot, and Isaac Sim digital twins let makers, students and small businesses teach and constrain robots around specific tasks, rather than waiting for a closed general-purpose humanoid.
Cost Per Token Is Replacing FLOPS as the AI Infrastructure Metric
Shruti Koparkar of NVIDIA’s Accelerated Computing team argues that AI infrastructure should be evaluated by token economics rather than by GPU-hour pricing or FLOPS per dollar. On NVIDIA’s AI Podcast, she lays out a four-part framework — token utility, supply, demand and monetization — in which cost per token becomes the central measure of business value. Koparkar says NVIDIA Blackwell’s system-level design delivers 50 times more tokens per watt than Hopper and 35 times lower token cost, while lower token costs will expand GPU demand by making more AI workloads economically viable.
Snap Cut Experimentation Job Costs 76% With GPU-Accelerated Spark
Prudhvi Vatala, Snap’s head of engineering platforms, argues that the company’s 10-plus-petabyte daily experimentation pipeline became a cost and scale problem that could not be solved by adding more CPUs. In an NVIDIA AI Podcast interview, he says Snap cut job costs by 76% by moving Spark workloads to NVIDIA GPU-accelerated infrastructure on Google Cloud, reusing idle inference GPUs overnight, and doing so without application code changes.
Enterprise AI Agents Need Harnesses, Traces, and Controlled Runtimes
LangChain co-founder and CEO Harrison Chase argues that enterprise AI agents are becoming an architectural problem rather than a question of adding autonomy wherever possible. In an NVIDIA AI Podcast interview, he says systems such as Claude Code, Manus and Deep Research share a common “deep agent” pattern: an LLM in a tool-calling loop, supported by a reusable harness, workspace, subagents and planning. For enterprises, Chase says trust depends on choosing the right level of autonomy and surrounding agents with observability, evaluation, secure runtimes and continued iteration.