Physical AI is more than robots, says Nvidia veteran turned investor

Amulya Vishwanath, who spent seven years leading product marketing for NVIDIA’s robotics platforms, has moved from the tech giant to investing in physical AI. She argues that physical AI is more than just robots. Her experience at NVIDIA gives her a unique perspective on the current state of robotics and artificial intelligence. This background informs her view that the field is still in its early stages.

Vishwanath says robotics is currently in its “Lego era,” a phase where the fundamental building blocks are still being developed and standardized. These building blocks include actuators, robotic hands, software, and safety standards. According to her, these components are being rebuilt, indicating that the field is not yet ready for mass adoption. The analogy suggests that significant foundational work remains before robotics can achieve widespread use.

This view stands in contrast to the belief held by some that robotics, powered by a general foundation model, is on the verge of a major breakthrough. Proponents argue that advances in AI, particularly in large language models and foundation models, will soon enable robots to perform a wide range of tasks with minimal programming. However, Vishwanath’s perspective implies that such expectations may be premature.

Vishwanath’s investment focus on physical AI suggests she sees potential beyond robots. Physical AI refers to artificial intelligence systems that interact with the physical world, which includes robots but also other embodied systems. Her background in marketing NVIDIA’s robotics platforms, which include hardware and software for autonomous machines, positions her to identify promising opportunities in this space.

By comparing robotics to the Lego era, Vishwanath highlights the need for standardized, interoperable components that can be easily assembled. This contrasts with the current state of robotics, where many systems are custom-built and lack common standards. The rebuilding of basic building blocks like actuators, robotic hands, software, and safety standards is a necessary step before robotics can achieve widespread adoption.

In summary, Vishwanath’s view challenges the optimism surrounding foundation models as a quick fix for robotics. She believes that physical AI is a broader field that requires foundational work before it can reach its full potential. Her transition from NVIDIA to investing in physical AI underscores her confidence in the long-term prospects of the industry, while acknowledging the significant challenges that lie ahead.