Nvidia CEO Jensen Huang is positioning robotics as the company’s next major growth frontier, following its meteoric rise to a $3 trillion market capitalization fueled by AI chip demand. At Computex 2024 in Taipei, Huang declared that “Physical AI is here” while unveiling Nvidia’s ambitious vision for two high-volume robotic products: self-driving cars and humanoid robots.
The strategic pivot comes as Nvidia faces the cyclical nature of the semiconductor industry. With data centers comprising 87% of the company’s revenue, Nvidia needs diversification to maintain its dominant position. The convergence of machine learning technologies has made robotics increasingly viable, as both autonomous vehicles and humanoid robots require humanlike perception, instantaneous reactions, and massive AI computing power—Nvidia’s core strength.
However, scaling robotics presents unprecedented challenges. “Robotic AI is the most complicated because a large language model is software, but robots are a mechanical-engineering problem, a software problem, and a physics problem,” explained Raul Martynek, CEO of DataBank. Unlike language-based AI, which has become accessible to non-experts through foundation models, robotics still requires doctoral-level expertise.
Nvidia has assembled a comprehensive robotics stack to lower barriers to entry: Omniverse (simulation platform), Isaac (training “gym”), Jetson Thor (robotics chip), Project Groot (foundation model for humanoids), and Osmo (integration software). The company launched a humanoid-robot developer program in July, offering tools to approved applicants. Major companies including BMW, Boston Dynamics, BYD Electronics, Figure, and Siemens are already using Nvidia’s platforms.
Yet three robotics experts told Business Insider that Nvidia hasn’t successfully democratized robot building like it did with AI applications. Competitors like Scaled Foundations (with its Grid platform) and Skild AI (which raised $300 million in July) are racing to create more accessible alternatives. Some developers worry about vendor lock-in, comparing Nvidia’s approach to “the Apple effect.”
The technical hurdles remain formidable. Gathering training data for humanoid movement is expensive and time-consuming—Tesla pays workers $48 per hour to perform tasks in special suits to train its Optimus robot. William Blair analysts project Nvidia’s automotive business will grow 20% annually through 2027, but the path to widespread humanoid adoption remains uncertain. Some roboticists question whether humanoid form factors are optimal, suggesting specialized single-task robots might prove more practical and cost-effective.
Key Quotes
Robotics is here. Physical AI is here. This is not science fiction.
Jensen Huang declared this at Computex 2024 in Taipei, signaling Nvidia’s strategic commitment to making robotics a core business pillar beyond its current AI chip dominance.
Robotic AI is the most complicated because a large language model is software, but robots are a mechanical-engineering problem, a software problem, and a physics problem. It’s much more complicated.
Raul Martynek, CEO of DataBank, explained why scaling robotics presents far greater challenges than the generative AI boom, highlighting the multidisciplinary complexity that makes democratization difficult.
The easiest robot to adapt in the world are humanoid robots because we built the world for us. There’s more data to train these robots because we have the same physique.
Huang’s rationale for focusing on humanoid form factors at Computex, arguing that human-shaped robots can leverage existing infrastructure and training data more effectively than alternative designs.
The way I’ve seen it happen in AI, the actual solution came from the community when they worked on something together. That’s when the magic started to happen, and this needs to happen in robotics right now.
Ashish Kapoor, founder of Scaled Foundations and former Microsoft robotics leader, emphasized that collaborative open development—not proprietary platforms—drove AI’s breakthrough, and robotics needs the same approach.
Our Take
Nvidia’s robotics ambitions reveal both visionary thinking and existential necessity. While Huang’s track record of anticipating AI trends is impressive, the robotics challenge is fundamentally different from selling GPUs for training language models. The company must navigate hardware complexity, real-world safety requirements, and entrenched industrial players—all while maintaining the open ecosystem that fueled its AI success.
The tension between platform control and community collaboration will likely determine outcomes. If Nvidia repeats its AI playbook of enabling widespread innovation while capturing infrastructure value, robotics could indeed become its next trillion-dollar opportunity. However, the “robotics startup graveyard” Kapoor mentioned suggests that technical challenges remain formidable. The real test isn’t whether Nvidia can build impressive demos, but whether it can make robot development accessible enough to spark the same Cambrian explosion of applications we’ve seen in generative AI. The next few years will reveal whether physical AI follows software AI’s trajectory—or requires an entirely different approach.
Why This Matters
This story represents a critical inflection point for both Nvidia and the broader AI industry. As the AI chip boom matures, Nvidia’s ability to maintain its trillion-dollar valuation depends on finding new high-volume markets beyond data centers. Physical AI and robotics represent the next frontier where machine learning meets the real world, with implications far beyond Nvidia’s balance sheet.
The convergence of AI with robotics could fundamentally transform manufacturing, logistics, transportation, and countless other industries. If Nvidia succeeds in democratizing robot development as it did with AI applications, we could see an explosion of robotic innovation similar to the generative AI boom. However, the technical complexity—combining mechanical engineering, software, and physics—makes this transition far more challenging than previous AI breakthroughs.
The competitive dynamics also matter significantly. Whether Nvidia can establish platform dominance in robotics or faces fragmentation from competitors will shape the industry’s development trajectory. An open, accessible ecosystem could accelerate innovation, while proprietary lock-in might slow adoption. For businesses, workers, and society, the stakes are enormous: humanoid robots and autonomous systems could revolutionize productivity while raising profound questions about employment, safety, and human-machine interaction.
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Source: https://www.businessinsider.com/jensen-huang-nvidia-robots-huamanoid-self-driving-cars-2024-9