Nvidia-watchers had plenty to celebrate at CES this week, with news that the company’s latest GPU, Vera Rubin, is now fully in production. Those powerful AI chips—the picks and shovels of the AI boom—are, after all, what helped make Nvidia the world’s most valuable company.
But in his keynote address, CEO Jensen Huang once again made clear that Nvidia does not see itself as simply a chip company. It is also a software company, with its reach extending across nearly every layer of the AI stack—and with a major bet on physical AI: AI systems that operate in the real world, including robotics and self-driving cars.
In a press release touting Nvidia’s CES announcements, a quote attributed to Huang declared that “the ChatGPT moment for robotics is here.” Breakthroughs in physical AI—models that understand the real world, reason, and plan actions—“are unlocking entirely new applications,” he said.
In the keynote itself, however, Huang was more measured, saying the ChatGPT moment for physical AI is “nearly here.” It might sound like splitting hairs, but the distinction matters—especially given what Huang said at last year’s CES, when he introduced Nvidia’s Cosmos world platform and described robotics’ “ChatGPT moment” as merely “around the corner.”
So has that moment really arrived, or is it still stubbornly out of reach?
Huang himself seemed to acknowledge the gap. “The challenge is clear,” he said in yesterday’s keynote. “The physical world is diverse and unpredictable.”
Nvidia is also no flash in the pan when it comes to physical AI. Over the past decade, the company has laid the groundwork by developing an ecosystem of AI software, hardware, and simulation systems for robots and autonomous vehicles. But it has never been about building its own robots or AVs. As Rev Lebaredian, Nvidia’s vice president of simulation technology, told Fortune last year, the strategy is still about supplying the picks and shovels.
There’s no doubt that Nvidia has progressed in that regard over the past year. On the self-driving front, today it unveiled the Alpamayo family of open AI models, simulation tools and datasets meant to help AVs safely operate across a range of rare, complex driving scenarios, which are considered the some of the toughest challenges for autonomous systems to safely master.
Nvidia also released new Cosmos and GR00T open models and data for robot learning and reasoning, and touted companies including Boston Dynamics, Caterpillar, Franka Robots, Humanoid, LG Electronics and NEURA Robotics, which are debuting new robots and autonomous machines built on Nvidia technologies.
Even with increasingly capable models, simulation tools, and computing platforms, Nvidia is not building the self-driving cars or the robots themselves. Automakers still have to turn those tools into systems that can safely operate on public roads—navigating regulatory scrutiny, real-world driving conditions, and public acceptance. Robotics companies, meanwhile, must translate AI into machines that can reliably manipulate the physical world, at scale, and at a cost that makes commercial sense.
That work—integrating hardware, software, sensors, safety systems, and real-world constraints—remains enormously difficult, slow, and capital-intensive. And it’s far from clear that faster progress in AI alone is enough to overcome those hurdles. After all, the ChatGPT moment wasn’t just about the model under the hood. Those had existed for several years. It was about the user experience and a company that was able to capture lightning in a bottle.
Nvidia has captured lightning in a bottle before—GPUs turned out to be the unlikely but perfect engine for modern AI. Whether that kind of luck can be repeated in physical AI, a far messier and less standardized domain, is still an open question.
