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Nvidia is betting on robotics as its next big growth driver because the world's Most worthy semiconductor company faces increasing competition in its core artificial intelligence chipmaking business.
The US tech giant, best known for the infrastructure that has supported the AI ​​boom, will launch its latest generation of compact computers for humanoid robots – called Jetson Thor – in the primary half of 2025.
Nvidia is positioning itself because the leading platform for what the technology group believes is an impending robotics revolution. The company sells a “full-stack” solution, from the software layers used to coach AI-powered robots to the chips built into them.
“The ChatGPT moment for physical AI and robotics is upon us,” Deepu Talla, Nvidia’s vp of robotics, told the Financial Times, adding that he believes the market has reached a “tipping point.”
The push into robotics comes as Nvidia faces increasing competition for its powerful AI chips from rival chipmakers equivalent to AMD, in addition to cloud computing giants equivalent to Amazon, Microsoft and Google, which want to reduce their dependence on the US semiconductor giant.
Nvidia, whose value has risen to over $3 trillion on account of huge demand for its AI chips, has positioned itself as an investor within the “physical AI” space to assist grow the subsequent generation of robotics firms.
In February, it was one in every of several firms, including Microsoft and OpenAI, to speculate $2.6 billion in humanoid robotics company Figure AI.
Robotics has up to now remained an emerging area of interest that shouldn’t be yet generating major returns. Many startups on this space are struggling to scale, reduce costs, and increase the accuracy of robotic products.
Nvidia doesn't provide a breakdown of robotics product sales, but they currently represent a comparatively small share of total revenue. Data center sales, which include the coveted AI GPU chips, accounted for about 88 percent of the group's total revenue of $35.1 billion within the third quarter.
However, Talla said a shift within the robotics market is being driven by two technological breakthroughs: the explosion of generative AI models and the power to coach robots using simulated environments based on these basic models.
The latter is a very significant development since it helps close what roboticists call the “sim-to-real gap” and be certain that robots trained in virtual environments can work effectively in the actual world, he said.
“In the last 12 months. . . (This gap) has matured to the purpose where we are able to now do simulation experiments combined with generative AI, which was impossible two years ago,” Talla said. “We provide the platform that permits all of those firms to do all of these items.”
Talla joined Nvidia in 2013 to work on its “Tegra” chip, originally intended for the smartphone market. However, the corporate quickly modified, and Talla oversaw the redeployment of about 3,000 engineers into “AI and autonomous training (e.g. for vehicles).” This was the origin of Jetson, Nvidia's line of robotic “brain” modules that launched in 2014.
Nvidia offers tools in three phases of robotics development: basic model training software, derived from Nvidia's “DGX” system; simulations of real-world environments in its “Omniverse” platform; and the hardware that enters the robots as “brains”.
Apptronik, which relies on Nvidia's technology to develop humanoid robots, also announced a strategic partnership with Google DeepMind in December to enhance its products.
According to US market researchers BCC, the worldwide robotics market is currently valued at around $78 billion and is predicted to succeed in $165 billion by the tip of 2029.
Amazon has already used Nvidia's robotics simulation technology for 3 of its warehouses within the US, and Toyota and Boston Dynamics are other customers using Nvidia's training software.
David Rosen, who directs the Robust Autonomy Lab at Northeastern University, said the robotics market still faces major challenges, including training the models and verifying their safety in use.
“Currently we wouldn’t have very effective tools for checking the protection and reliability properties of machine learning systems, especially in robotics. “This is a vital open scientific query on this field,” Rosen said.
