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Nvidia is betting on robotics as its next big driver of growth, as the world’s most valuable semiconductor company faces increasing competition in its core artificial intelligence chipmaking business.
The US tech giant, best known for the infrastructure that has underpinned the AI boom, is set to launch its latest generation of compact computers for humanoid robots — dubbed Jetson Thor — in the first half of 2025.
Nvidia is positioning itself to be the leading platform for what the tech group believes is an imminent robotics revolution. The company sells a “full stack” solution, from the layers of software for training AI-powered robots to the chips that go into them.
“The ChatGPT moment for physical AI and robotics is around the corner,” Deepu Talla, Nvidia’s vice-president of robotics, told the Financial Times, adding that he believes the market has reached a “tipping point”.
The push into robotics comes as Nvidia is experiencing more competition for its powerful AI chips from rival chipmakers such as AMD, as well as cloud computing giants such as Amazon, Microsoft and Google, who are seeking to reduce their dependence on the US semiconductor giant.
Nvidia, whose valuation has soared past $3tn on the back of huge demand for its AI chips, has positioned itself as an investor in the ‘physical AI’ space, in a bid to help grow the next generation of robotics companies.
In February, it was one of several companies, including Microsoft and OpenAI, to invest in humanoid robotics company Figure AI at a $2.6bn valuation.
Robotics has so far remained an emerging niche that has yet to generate large returns. Many start-ups in the space are struggling with scaling, reducing costs and increasing the accuracy of robot products.
Nvidia does not break out robotics product sales, but it currently represents a relatively small share of overall revenues. Data centre revenue, which includes its sought-after AI GPU chips, made up about 88 per cent of its overall sales of $35.1bn in the group’s third quarter.
But Talla said a shift in the robotics market is being driven by two technological breakthroughs: the explosion of generative AI models and the ability to train robots on these foundational models using simulated environments.
The latter has been a particularly significant development as it helps solve what roboticists call the “Sim-to-Real gap”, ensuring robots trained in virtual environments can operate effectively in the real world, he said.
“In the past 12 months . . . [this gap] has matured sufficiently that we can now carry out experiments in simulation, combining with generative AI, that we could not do two years ago,” said Talla. “We provide the platform for enabling all of these companies to do any of those tasks.”
Talla joined Nvidia in 2013 to work on its ‘Tegra’ chip, which was initially aimed at the smartphone market. However, the company quickly pivoted, with Talla overseeing the redeployment of about 3,000 engineers into “AI and autonomous training [for vehicles, for example].” This was the genesis of Jetson, Nvidia’s line of robotic ‘brain’ modules that emerged in 2014.
Nvidia offers tools at three stages of robotics development: software for training foundational models, which comes from Nvidia’s ‘DGX’ system; simulations of real-world environments in its ‘Omniverse’ platform; and the hardware to go inside the robots as its ‘brain.’
Apptronik, which uses Nvidia’s technology throughout its development of humanoid robots, in December also announced a strategic partnership with Google DeepMind to improve its products.
The global robotics market is currently valued at about $78bn, according to US market researchers BCC, and is projected to reach $165bn by the end of 2029.
Amazon has already deployed Nvidia’s robotics simulation technology for three of its warehouses in the US, and Toyota and Boston Dynamics are among other customers using Nvidia’s training software.
David Rosen, who leads the Robust Autonomy Lab at Northeastern University, said the robotics market still faces significant challenges, including training the models and verifying they will be safe when deployed.
“As of right now, we don’t have very effective tools for verifying the safety and reliability properties of machine learning systems, especially in robotics. This is a major open scientific question in the field,” said Rosen.
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