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Reading: Nvidia’s Jensen Huang says the “ChatGPT second” for robotics is sort of right here — however is it?
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Nvidia’s Jensen Huang says the “ChatGPT second” for robotics is sort of right here — however is it?
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Nvidia’s Jensen Huang says the “ChatGPT second” for robotics is sort of right here — however is it?

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Last updated: January 6, 2026 4:48 pm
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Published: January 6, 2026
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Nvidia-watchers had loads to have a good time at CES this week, with information that the corporate’s newest GPU, Vera Rubin, is now absolutely in manufacturing. These highly effective AI chips—the picks and shovels of the AI increase—are, in spite of everything, what helped make Nvidia the world’s most dear firm.

However in his keynote deal with, CEO Jensen Huang as soon as once more made clear that Nvidia doesn’t see itself as merely a chip firm. It’s also a software program firm, with its attain extending throughout almost each layer of the AI stack—and with a serious guess on bodily AI: AI methods that function in the true world, together with robotics and self-driving vehicles.

In a press launch touting Nvidia’s CES bulletins, a quote attributed to Huang declared that “the ChatGPT second for robotics is right here.” Breakthroughs in bodily AI—fashions that perceive the true world, cause, and plan actions—“are unlocking solely new purposes,” he mentioned.

Within the keynote itself, nevertheless, Huang was extra measured, saying the ChatGPT second for bodily AI is “almost right here.” It would sound like splitting hairs, however the distinction issues—particularly given what Huang mentioned finally yr’s CES, when he launched Nvidia’s Cosmos world platform and described robotics’ “ChatGPT second” as merely “across the nook.”

So has that second actually arrived, or is it nonetheless stubbornly out of attain?

Huang himself appeared to acknowledge the hole. “The problem is evident,” he mentioned in yesterday’s keynote. “The bodily world is numerous and unpredictable.”

Nvidia can also be no flash within the pan with regards to bodily AI. Over the previous decade, the corporate has laid the groundwork by growing an ecosystem of AI software program, {hardware}, and simulation methods for robots and autonomous autos. Nevertheless it has by no means been about constructing its personal robots or AVs. As Rev Lebaredian, Nvidia’s vice chairman of simulation know-how, advised Fortune final yr, the technique remains to be about supplying the picks and shovels.

There’s little question that Nvidia has progressed in that regard over the previous yr. On the self-driving entrance, right this moment it unveiled the Alpamayo household of open AI fashions, simulation instruments and datasets meant to assist AVs  safely function throughout a variety of uncommon, complicated driving eventualities, that are thought-about the among the hardest challenges for autonomous methods to soundly grasp. 

Nvidia additionally launched new Cosmos and GR00T open fashions and information for robotic studying and reasoning, and touted corporations together with Boston Dynamics, Caterpillar, Franka Robots, Humanoid, LG Electronics and NEURA Robotics, that are debuting new robots and autonomous machines constructed on Nvidia applied sciences.

Even with more and more succesful fashions, simulation instruments, and computing platforms, Nvidia just isn’t constructing the self-driving vehicles or the robots themselves. Automakers nonetheless have to show these instruments into methods that may safely function on public roads—navigating regulatory scrutiny, real-world driving situations, and public acceptance. Robotics corporations, in the meantime, should translate AI into machines that may reliably manipulate the bodily world, at scale, and at a value that makes business sense.

That work—integrating {hardware}, software program, sensors, security methods, and real-world constraints—stays enormously tough, sluggish, and capital-intensive. And it’s removed from clear that sooner progress in AI alone is sufficient to overcome these hurdles. In any case, the ChatGPT second wasn’t simply in regards to the mannequin below the hood. These had existed for a number of years. It was in regards to the consumer expertise and an organization that was in a position to seize lightning in a bottle. 

Nvidia has captured lightning in a bottle earlier than—GPUs turned out to be the unlikely however good engine for contemporary AI. Whether or not that type of luck will be repeated in bodily AI, a far messier and fewer standardized area, remains to be an open query.

Be part of us on the Fortune Office Innovation Summit Could 19–20, 2026, in Atlanta. The following period of office innovation is right here—and the previous playbook is being rewritten. At this unique, high-energy occasion, the world’s most modern leaders will convene to discover how AI, humanity, and technique converge to redefine, once more, the way forward for work. Register now.
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