Building AI for the real world is a very different problem than building AI for text. I sat down with Steve Xie, Founder & CEO of Lightwheel on The Ravit Show, to break down what it actually takes to train systems that operate in the physical world. Steve’s journey from Peking University to Columbia University, and then into leadership roles at Cruise, NVIDIA, and NIO, gives him a unique lens into where today’s AI systems struggle when they leave controlled environments and face the real world.

One of the biggest takeaways from this conversation is that the core bottleneck in AI is no longer models, it is data. While large language models benefited from massive, passive data sources, robotics has no equivalent. There is no scalable way to collect real-world interaction data, no reliable evaluation layer, and very little infrastructure to continuously improve systems once deployed.

This is where simulation becomes critical. In autonomous driving, simulation is helpful. In robotics, it is foundational. You cannot run thousands of parallel experiments in the real world, and you cannot reset physical environments at will. Simulation is what makes learning, testing, and iteration possible at scale. But not everything that looks like simulation actually works. As Steve explains, true simulation needs to be physically accurate, reproducible, and capable of generating actionable feedback. Without that, it cannot train real systems.

What makes Lightwheel interesting is their approach to solving this problem. Instead of starting with data collection, they start with evaluation. They identify where models fail, generate targeted data to fix those failures, and create a continuous feedback loop. It is a shift from a passive data pipeline to an active data engine built for physical AI.

They are already working with teams like DeepMind, ByteDance, and Alibaba, building infrastructure that sits beneath both robotics companies and AI labs.

The bigger idea is simple. You cannot scrape your way to physical intelligence. You have to generate, test, and refine data in closed loops.

#data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow