The world’s most valuable semiconductor company, Nvidia, is placing a significant wager on robotics as its next major growth catalyst whilst facing mounting competition in its core artificial intelligence chipmaking operations.
The US tech giant is readying to unveil its latest generation of compact computers for humanoid robots, dubbed Jetson Thor, in early 2025. This strategic move positions Nvidia to become the dominant platform provider for what it perceives as an imminent robotics revolution.
The company’s comprehensive approach includes offering a complete solution stack, ranging from AI robot training software layers to the essential semiconductors powering these machines. Deepu Talla, Nvidia’s vice-president of robotics, expressed his conviction about the industry reaching a crucial milestone, stating, “The ChatGPT moment for physical AI and robotics is around the corner.”
This strategic pivot comes as Nvidia experiences heightened competition in its AI chip market from rivals like AMD and major cloud computing providers including Amazon, Microsoft and Google, who are actively seeking to reduce their reliance on Nvidia’s products.
The company, whose market capitalisation has surged beyond £3 trillion due to unprecedented demand for its AI chips, has emerged as a key investor in the “physical AI” sector. This investment strategy aims to nurture the next wave of robotics enterprises, exemplified by its February investment in Figure AI, alongside Microsoft and OpenAI, at a £2.6 billion valuation.
Whilst robotics currently represents a modest portion of Nvidia’s revenue stream, with data centre operations accounting for approximately 88 per cent of its £35.1 billion third-quarter sales, the sector’s potential appears promising. Industry analysts project the global robotics market to expand from its current £78 billion valuation to £165 billion by 2029.
The transformation in robotics is being propelled by two key technological advances: the proliferation of generative AI models and enhanced robot training capabilities through simulated environments. These developments have significantly narrowed the “Sim-to-Real gap,” enabling more effective real-world robot deployment.
Despite the optimistic outlook, challenges persist in the robotics sector, particularly regarding model training and safety verification. David Rosen of Northeastern University’s Robust Autonomy Lab emphasises the ongoing scientific challenge of developing effective tools for verifying the safety and reliability of machine learning systems in robotics applications.
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