Foxconn to deploy humanoid robots at Houston plant making Nvidia AI servers

Source Cryptopolitan

Foxconn, the biggest electronics manufacturer in the world, announced Tuesday it plans to bring human-like robots into its Houston facility that makes AI servers for Nvidia. The move marks a major step in bringing advanced automation to American manufacturing floors.

The Taiwan-based company said the Texas plant will be one of the first to use these machines on its assembly lines. The robots will run on technology from Nvidia called the Isaac GR00T N model. Both companies want to create what they’re calling a top-tier AI smart factory.

Back in June, Reuters broke the story that Foxconn and Nvidia were discussing putting these robots to work at the Houston location. At the time, the companies were shooting for the first three months of 2026 as their target date.

Foxconn, whose full name is Hon Hai Precision Industry Co., also said it will keep ramping up production of AI servers across Texas, Wisconsin, and California. The company wants to keep pace with growing customer needs.

“Our team is bringing the most advanced AI data center solutions to the United States, which will help our leading customers stay ahead in the AI race,” said Foxcon chair Young Liu, who chairs Foxconn. The company made the announcement at Nvidia’s developers’ conference happening in Washington, D.C, Reuters reported.

American companies race to catch up

Tech firms across America are pushing hard to develop these human-shaped robots, saying they’re critical for the future. However, experts are warning that American companies might already be falling behind rivals in China.

These robots use artificial intelligence and are built to look and move like people. Industry watchers expect them to fill various roles, including factory jobs and service industry positions.

People with money to invest are getting more excited about these machines. Tech executives like Jensen Huang from Nvidia have been talking them up. In March, Huang declared the start of “the age of generalist robotics” while unveiling new tools for building these robots.

When it comes to actually making the robots, Tesla appears to be out front in America. The company’s robot project, called Optimus, has big plans.  Even with Musk’s aggressive timeline potentially giving Tesla an edge over American rivals like Apptronik and Boston Dynamics, which haven’t reached large-scale production yet, he faces tough competition from China.

China’s price advantage

Morgan Stanley put out research in February looking at what it costs to build these robots. They figured the price could run anywhere from $10,000 to $300,000 per robot, depending on how they’re configured and what they’ll be used for.

Chinese companies are already beating American prices thanks to better manufacturing scale and production know-how. Take Unitree, which released its G1 robot for consumers in May, starting at $16,000. By comparison, Morgan Stanley thinks Tesla’s Optimus Gen2 might sell for around $20,000, but only if Tesla can increase production, speed up development, and use cheaper parts from China.

Unitree grabbed attention in January when 16 of its top-performing H1 robots danced alongside human performers during Lunar New Year celebrations broadcast on national TV.

China’s lead goes beyond just pricing. Morgan Stanley’s February report showed China filed 5,688 patents mentioning “humanoid” over the past five years. The United States filed just 1,483 during the same period.

Big Chinese companies like Xiaomi and electric vehicle makers, including BYD, Chery, and Xpeng, are all working on human-like robots too.

As reported by Cryptopolitan recently, Cathie Wood believes robots that look and act like humans could become the biggest opportunity in artificial intelligence.

Wood, who runs Ark Invest, went further with her prediction. “I think the chaser is going to be humanoid robots. And I think that is going to be the biggest of all the embodied AI opportunities,” she said. However, some experts warn that the timeline for widespread adoption may be longer than many investors expect, with significant technical and deployment challenges still ahead.

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