OpenAI's ChatGPT learned from decades of internet-scale data. Humanoid robots don't have that luxury. As investors pour billions into embodied AI, comparisons between robots and large language models have become increasingly common.

But Jerry Wang, Global Executive Chairman of Faraday Future Intelligent Electric Inc. (FFAI) and CEO of AIxCrypto Holdings, Inc. (AIXC), says there are fundamental differences.

While ChatGPT analyzes text, images, and audio to predict the next best word, warehouse robots process force sensors, cameras, and grid layouts to determine the next best motion. That distinction could help explain why the next phase of the robotics race may be less about building smarter AI models and more about collecting real-world data.

The Data Gap

Large language models like ChatGPT benefited from an enormous head start. "They benefited from decades of internet-scale data," Wang told MarketDash. "Robots don't have that advantage. They learn by interacting with the physical world."

Every warehouse they navigate, every package they move and every task they complete generates operational data that can improve future performance. But unlike internet text, that data isn't readily available.

"It's harder, slower, and more expensive to collect," Wang said. That means scaling embodied AI isn't simply a matter of building larger models or adding more computing power. Companies first need robots operating in the real world, learning from real-world environments.

Deployment Becomes the Advantage

For Wang, that's where the industry's next competitive edge could emerge.

The more robots deployed, the more operational data companies can collect. More data leads to better performance, making robots increasingly valuable to businesses and encouraging wider adoption—a feedback loop that's fundamentally different from the one that fueled generative AI.

"We believe that as more robots are deployed and used more frequently, the broader ecosystem has the potential to benefit," Wang said. "More deployment leads to more data, better performance, stronger ROI, and ultimately broader adoption."

That suggests deployment itself may become a strategic asset, not just a measure of commercial success.

The Next AI Race

Much of today's robotics conversation centers on who is building the smartest humanoid. Tesla, Inc. (TSLA), Figure AI, Unitree and Boston Dynamics continue competing to improve mobility, intelligence and real-world capabilities.

But if Wang is right, the companies with the biggest long-term advantage may not simply be those building the best robots today. They could be the ones putting the most robots into real-world environments tomorrow, creating proprietary datasets that competitors can't easily replicate.

For investors, that's an important distinction. The generative AI boom rewarded companies with access to massive amounts of digital data. The robotics boom may ultimately reward those with access to something much harder to acquire: millions of hours of real-world experience.

Image created using artificial intelligence via ChatGPT