- Developers
- Learn about the challenges and opportunities of building and deploying AI models on resource-constrained edge hardware.
- Business
- Discover how AI is driving efficiency and new capabilities in asset-heavy industries like logistics, construction, and field service.
This is a solid interview with a CEO scaling AI in the physical world, offering practical insights into a major AI application area.
Why the Next AI Revolution Will Happen Off-Screen: Samsara CEO Sanjit Biswas
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Sequoia Capital's Training Data podcast features Samsara CEO Sanjit Biswas discussing AI's next revolution happening off-screen. Biswas explains how physical AI differs from cloud AI, focusing on edge devices, real-world data diversity, and new capabilities like video reasoning. He highlights how AI is improving frontline worker performance in asset-heavy industries.
This episode explores how AI is being used in the real world, like in trucks and construction sites, not just on computers. It explains how AI can help workers do their jobs better and make things more efficient, even with messy, real-life data.
This episode offers insights into the practical application of AI beyond screens, a crucial area for industries like logistics and construction. It's for business leaders and technologists interested in how AI drives efficiency in physical operations. The discussion on distributed edge compute and foundation models for video reasoning connects to ongoing research and development in multimodal AI by frontier labs.
- HostSequoia Capital
- GuestSanjit BiswasCEO, Samsara
- Guest: Sanjit Biswas, CEO of Samsara
- Show: Training Data (Sequoia Capital)
- Length: 38 minutes
- Samsara's scale: Captures 90 billion miles of driving data annually
- Samsara's market cap: $20B+ public company
Sanjit Biswas is one of the rare founders who has scaled AI in the physical world – first with Meraki, and now with Samsara, a $20B+ public company with sensors deployed across millions of vehicles and job sites. Capturing 90 billion miles of driving data each year, Samsara operates at a scale matched only by a small handful of companies. Sanjit discusses why physical AI is fundamentally different from cloud-based AI, from running inference on two- to ten-watt edge devices to managing the messy diversity of real-world data—weather, road conditions, and the long tail of human behavior.
He also shares how advances in foundation models unlock new capabilities like video reasoning, why distributed compute at the edge still beats centralized data centers for many autonomy workloads, and how AI is beginning to coach frontline workers—not just detect risk, but recognize good driving and improve fuel efficiency. Sanjit also explains why connectivity, sensors, and compute were the original “why now” for Samsara, and how those compounding curves will reshape logistics, field service, construction, and every asset-heavy industry.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
- inference
- Sanjit Biswas
- The process of an AI model making a prediction or decision based on new data.
- edge devices
- Sanjit Biswas
- Small, low-power computing devices located close to where data is generated, like in vehicles.
- foundation models
- Sanjit Biswas
- Large, general-purpose AI models that can be adapted for various specific tasks.
- video reasoning
- Sanjit Biswas
- The ability of AI to understand and interpret the content of video footage.
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Questions people ask
- What is physical AI?
- Physical AI refers to AI applications that operate in the real world, often using sensors on devices like vehicles or job sites, rather than solely in cloud-based systems.
- How is AI improving frontline workers?
- AI can coach frontline workers by detecting not only risks but also recognizing good performance, such as improving driving habits for better fuel efficiency.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Sequoia Capital.


